Genetically Encoded Fluorescent Biosensors ... - ACS Publications

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Cite This: Chem. Rev. 2018, 118, 11707−11794

Genetically Encoded Fluorescent Biosensors Illuminate the Spatiotemporal Regulation of Signaling Networks Eric C. Greenwald,† Sohum Mehta,*,† and Jin Zhang*,† †

University of California, San Diego, 9500 Gilman Drive, BRFII, La Jolla, CA 92093-0702, United States

Chem. Rev. 2018.118:11707-11794. Downloaded from pubs.acs.org by UNIV OF WINNIPEG on 12/26/18. For personal use only.

S Supporting Information *

ABSTRACT: Cellular signaling networks are the foundation which determines the fate and function of cells as they respond to various cues and stimuli. The discovery of fluorescent proteins over 25 years ago enabled the development of a diverse array of genetically encodable fluorescent biosensors that are capable of measuring the spatiotemporal dynamics of signal transduction pathways in live cells. In an effort to encapsulate the breadth over which fluorescent biosensors have expanded, we endeavored to assemble a comprehensive list of published engineered biosensors, and we discuss many of the molecular designs utilized in their development. Then, we review how the high temporal and spatial resolution afforded by fluorescent biosensors has aided our understanding of the spatiotemporal regulation of signaling networks at the cellular and subcellular level. Finally, we highlight some emerging areas of research in both biosensor design and applications that are on the forefront of biosensor development.

CONTENTS 1. Introduction 2. Designing Genetically Encoded Biosensors 2.1. Elements of Genetically Encoded Biosensors 2.2. Biosensors for Monitoring Protein Behavior 2.2.1. Protein Expression, Turnover, And Localization 2.2.2. Protein−Protein Interactions 2.3. Biosensors for Monitoring Biochemical Activity Dynamics 2.3.1. Inducing the Translocation of a Fluorescent Protein 2.3.2. Directly Sensitizing the Chromophore of a Fluorescent Protein 2.3.3. Engineered Modulation of the Photophysical Behavior of a Fluorescent Protein 2.3.4. Coupled Reporter Systems 2.4. New Biosensor Classes 2.4.1. Sensors Based on Infrared Fluorescent Proteins 2.4.2. Biochemical Activity Integrators As in Vivo Snapshot Reporters 2.4.3. Fluctuation-Based Biosensors 3. Obtaining Temporal Information from Biosensors 3.1. Kinetics of Individual Reactions 3.2. Higher-Order Signaling Dynamics 3.2.1. Adaptive Responses 3.2.2. Oscillations 3.2.3. Bistability and Ultrasensitivity 3.3. Importance of Single-Cell Readouts © 2018 American Chemical Society

3.4. How Fast Can We Go? 4. Obtaining Spatial Information from Biosensors 4.1. Membrane-Associated Signaling Compartments 4.1.1. Partitioning by Membranes 4.1.2. Partitioning within Membranes 4.2. Compartmentation by the Activity of Pathway Regulators 4.2.1. Calcium Signaling 4.2.2. cAMP Signaling 4.2.3. Other Signaling Molecules 4.3. Assembly of Macromolecular Signaling Complexes 4.3.1. Scaffolding by A-Kinase Anchoring Proteins 4.3.2. Molecular Scaffolds in Other Signaling Pathways 5. Computational Models and Fluorescent Biosensors 5.1. Computational Modeling Background 5.2. Examples of the Integrated Approach 5.2.1. Analysis of Temporal Dynamics 5.2.2. Evaluation of Spatial Compartmentalization 5.2.3. Analyses of Cellular Heterogeneity 5.2.4. Information Theory 6. Pushing the Field Forward 6.1. Improved Resolution 6.2. Multiplexed Biosensor Imaging

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Received: May 25, 2018 Published: December 14, 2018 11707

DOI: 10.1021/acs.chemrev.8b00333 Chem. Rev. 2018, 118, 11707−11794

Chemical Reviews 6.3. Applications to More Complex Biological Systems 6.4. Translational Applications of Genetically Encodable Fluorescent Biosensors 7. Conclusion Associated Content Supporting Information Author Information Corresponding Authors ORCID Notes Biographies Acknowledgments References

Review

revolutionized by the isolation and engineering of fluorescent proteins (FPs).4−6 What began with the Aequorea victoria green fluorescent protein (GFP) has expanded through protein engineering and discoveries in other species to encompass a diverse assortment of FPs with unique photophysical properties (recently reviewed in ref 7). Currently, FPs span the colors of the rainbow and beyond, from the near-ultraviolet8 to the nearinfrared,9 with similarly varying brightness and quantum yield.7 In parallel to the development of FPs, a suite of technologies have been developed for in situ labeling of recombinant proteins (e.g., FlAsH, HaloTag, SNAP-Tag) (reviewed in ref 10). These technologies, particularly the development of FPs, opened up new avenues by facilitating the construction of genetically encoded fluorescent biosensors, which can be synthesized directly within cells, expressed in specific subsets of cell populations, and easily fused with different protein tags for targeting to specific subcellular microdomains. As such, a diverse array of genetically encoded fluorescent biosensors have been engineered to monitor the dynamics of various signaling pathway components, including cell-surface receptors, intracellular messengers, and enzymatic effector proteins. Here, we first discuss the different design strategies that have been exploited to develop genetically encoded fluorescent biosensors, including some novel biosensor classes that have been developed recently. To this end, we have endeavored to assemble a comprehensive list of published fluorescent biosensors, which includes over 750 different biosensor variants, as seen in Table 1 and with more detail online at BiosensorDB.ucsd.edu. We then review how fluorescent biosensors have been utilized to examine and dissect the spatiotemporal dynamics of signal transduction and, finally, highlight new avenues of exploration with fluorescent biosensors.

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1. INTRODUCTION One of the most fundamental aspects of life is the ability of cells to respond to external cues originating from other cells or from the environment. Doing so requires cells to exist in a state of dynamic equilibrium, constantly poised to unleash cascades of chemical reactions from which all complex biological phenomena emerge. Through this network of signaling pathways, cells alter their physical state and composition such that they can appropriately determine their fate and perform the functions essential to their role within an organism. In the postgenomic era, wherein the identities of most signaling pathway components are known, elucidating the relationships among these components, and thus the dynamics and regulation of signal transduction, has become one of the foremost avenues toward furthering our understanding of both physiological and pathological cellular function. Because these signaling reactions are often discrete and transitory in nature, the development of fluorescence-based methods capable of monitoring and quantifying signaling dynamics in real time in their endogenous cellular context has proven instrumental to these efforts. In many ways, fluorescence is an ideal tool for monitoring the behavior of signaling pathways within cells: fluorescence is both rapid, with the absorption and emission of light by a fluorescent molecule (i.e., fluorophore) occurring on the order of nanoseconds, and spatially precise, as the emitted wavelengths are smaller than many cellular structures. Thus, fluorescence-based readouts permit the high-fidelity recording of extremely rapid processes with single-cell and even subcellular spatial resolution. Combined with the development of increasingly sophisticated and sensitive microscopy hardware, fluorescence can be used to nondestructively visualize cellular processes directly in living cells, providing data with a combined temporal and spatial resolution that is not possible through traditional biochemical methods. Although fluorescence has served as a sensitive in situ label for the better part of a century, beginning with the synthesis of fluorophore-conjugated antibodies and the advent of immunofluorescence,1 the first bona fide fluorescent sensors capable of probing intracellular signaling dynamics emerged much later and included covalently labeled versions of endogenous proteins (i.e., “fluorescent analogs”, reviewed in ref 2) and organic indicator dyes (reviewed in ref 3). These synthetic probes enabled the first real-time measurements of dynamic changes in cellular parameters such as messenger and ion concentrations. However, this budding field was soon

2. DESIGNING GENETICALLY ENCODED BIOSENSORS 2.1. Elements of Genetically Encoded Biosensors

For the purposes of this review, we define genetically encoded fluorescent biosensors as chimeric proteins that can be expressed intracellularly and are engineered to act as sensors for monitoring signal transduction (Table 1). The basic function of any sensor is to translate information on a physical property or state of a system into a measurable readout. For fluorescent biosensors, this means converting diverse signaling activities, such as the concentration of intracellular messengers, metabolic compounds and other analytes, or the localization, conformations, and activity of signaling proteins into one of several types of fluorescence signals. Fluorescent biosensors are thus fundamentally defined by two essential components: a sensing unit which detects changes in signal transduction and a reporting unit which conveys these changes in a quantifiable form. The sensing unit is often derived from an endogenous cellular protein that participates in the signaling pathway of interest and is thus intrinsically sensitive to the target signaling event (e.g., calmodulin to sense changes in Ca2+ concentrations). In other cases, isolated protein domains or peptides sequences can also be combined to confer a biosensor with the desired sensitivity. Meanwhile, the reporting unit typically consists of one or more FP variants (or fragments thereof) coupled to the sensing unit in such a way that signalinginduced changes in the state of the sensing unit alter FP 11708


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Table 1. Engineered Genetically Encoded Fluorescent Biosensors Are Organized by Their Signaling Targetsa target

readout mech

variants

cell cycle cell cycle phases

Inten. (I☼)

Fucci: Fucci(CA)2.1 I☼ (2017)220 Fucci4 I☼ (2016)221 NIR Fucci I☼ (2016)222 NIR cell cycle reporter I☼ (2016)223 Fucci2.1 I☼ (2013)224 Fucci2 I☼ (2011)225 zFucci I☼ (2009)226 S/G2/M-X(NC) I☼ (2008)227 Fucci I☼ (2008)228

G1 phase

Trans. (T⇄)

G1 phase biosensor T⇄ (2009)229

S phase

Trans. (T⇄)

S phase biosensor T⇄ (2009)229

FRET (FR◆) Ex. Rat. (EX★)

HP35: HP35st-TS FR◆ (2015)230 HP35-TS FR◆ (2015)230 PriSSM EX★ (2008)231 stFRET: cpstFRET FR◆ (2012)232 sstFRET FR◆ (2011)233 stFRET TSMod: TSMod F25 FR◆ (2016)235 TSMod FR◆ (2010)236

cell environment mechanical strain

molecular crowding

FRET (FR◆)

crowding sensor

pH

Em. Rat. (EM☆) Ex. Rat. (EX★) FRET (FR◆) Inten. (I☼) Rat. (R◧)

deGFP: deGFP family

membrane voltage (V+)

BRET (BR◓) FRET (FR◆)

Inten. (I☼) Rat. (R◧)

FR

FR

◆ (2008)234

◆ (2015)237 ☆ (2002)134

EM

dual pH and Cl sensor: ClopHensor R◧ (2010)142 ExGFP: E1GFP R◧ (2007)141 E2GFP R◧ (2006)138 FluBpH: FluBpH 7.5 FR◆ (2017)238 FluBpH 6.1 FR◆ (2017)238 FluBpH 5.7 FR◆ (2017)238 mtAlpHi I☼ (2004)239 native FP pH sensitivity: mNect I☼ (2009)240 pHVenus I☼ (2007)129 CFP-YFP pH FRET FR◆ (2004)241 EYFP I☼ (1998)125 EGFP I☼ (1998)124 pHCEC: pHCECSensor01 R◧ (2008)242 pHluorins: pHluorin2 EX★ (2011)243 superecliptic-pHluorin I☼ (2000)126 ratiometric-pHluorin EX★ (1998)121 ecliptic-pHluorin I ☼ (1998)121 pHRed EX★ (2011)244 pHTomato I☼ (2012)127 pHuji I☼ (2014)245 pHusion R◧ (2012)246 SypHer: SypHer3s EX★(2018)996 SypHer-2 EX★ (2015)997 SypHer I☼ (2011)247 XFpH: YFpH FR◆ (2001)248 GFpH FR◆ (2001)248

ArcLight species variants: Human Q193 ArcLight I☼ (2013)249 Zebrafish Q175 ArcLight I☼ (2013)249 Chicken Q175 ArcLight I ☼ (2013)249 Frog Q174 ArcLight I☼ (2013)249 Ci-VSP based: VSD-FR189−188 I☼ (2017)250 LOTUS-V BR◓ (2017)251 Marina I☼ (2017)252 tdFlicR1 Δ110AR I☼ (2016)253 FlicGR1 I☼ (2016)253 ASAP 2f I☼ (2016)184 tdFlicR-VK-ASAP R◧ (2016)253 FlicR1 I☼ (2016)185 Bongwoori I☼ (2015)254 ArcLightning I☼ (2015)255 Nabi2.213 FR◆ (2015)256 ASAP1 I☼ (2014)175 Chimeric VSFP-Butterfly YR FR◆ (2014)257 Chimeric VSFP-Butterfly CY FR◆ (2014)257 Mermaid2 FR◆ (2013)258 ArcLight A242 I☼ (2012)259 Chimera Cx FR◆ (2012)260 VSFP-CR FR ◆ (2012)261 ElectricPk I☼ (2012)182 VSFP Butterfly 1.2 FR◆ (2012)262 VSFP2.3 FR◆ (2010)263 VSFP2.42 FR◆ (2010)264 VSFP2.4 FR◆ (2009)265 VSFP3.1 I☼ (2008)180 Mermaid FR◆ (2008)266 VSFP2.1 FR◆ (2007)267 Danio VSP based: Zahara 2 SE (227D) I☼ (2015)268 Zahra 1 SE (227D) series I☼ (2015)268 Zahra 2 FR◆ (2012)269 Zahra 1 FR◆ (2012)269 Kv based: FlaSH (CFP) + FlaSh (YFP) FR◆ (2002)270 FlaSh L366A I☼ (2002)270 FlaSh IR I☼ (2002)270 VSFP1 FR◆ (2001)271 FlaSh I☼ (1997)176 Na channel based: SPARC I☼ (2002)177 Nematostella VSP based: Nema FR◆ (2012)269 Rhodopsin based: Archon2 I☼ (2018)272 Archon1 I☼ (2018)272 VARNAM I☼ (2018)998 FRET-opsin Ace1Q-mNeon I☼ (2015)273 FRET-opsin Ace2N-mNeon I☼ (2015)273 QuasAr2 I☼ (2014)274 QuasAr1 I☼ (2014)274 Archer1 I☼ (2014)275 eFRET GEVI FR◆ (2014)276 FRET-opsin Mac-mCitrine I☼ (2014)277 Arch-EEx variants I☼ (2013)278 PROPS I☼ (2011)279 Arch (D95N) I☼ (2011)280

cellular analytes 2-oxoglutarate

Inten. (I☼)

mOGsor I☼ (2014)281

ammonium


AmTrac: AmTryoshika1;3-LS-F138I -T78H R◧ (2017)219 AmTryoshika1;3-LS-F138I R◧ (2017)219 AmTrac I☼ (2013)282

arabinose

FRET (FR◆)

FLIPara: FLIPara-250n

arginine

FRET (FR◆)

FRET Arg Reporter

ATP

Biolum. (BL●)

FR

FR

◆ (2008)283

◆ (2007)284

ATeam: BTeam BR◓ (2016)285 ATeam1.03NL FR◆ (2013)286 GO-Ateam FR◆ (2011)287 ATeam3.10 FR◆ (2009)288 ATeam1.03 ◆ (2009)288

FR

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Table 1. continued target

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variants

cellular analytes BRET (BR◓) Ex. Rat. (EX★) FRET (FR◆)

EAF-ATP FR◆ (2013)289 nanolantern ATP BL● (2012)290 QUEEN: QUEEN-2m EX★ (2014)291 QUEEN-7mu Syn-ATP BL● (2014)292

ATP:ADP ratio

Ex. Rat. (EX★)

Perceval: PercevalHR

BDNF

FRET (FR◆)

Bescell

cAMP

Biolum. (BL●) BRET (BR◓)

cADDis: cADDis-green I☼ (2016)191 Camps: Epac2-camps300-cit FR◆ (2010)296 Epac2-camps300 FR◆ (2009)297 HCN2-camps FR◆ (2006)298 Epac2-camps FR◆ (2004)299 Epac1-camps FR◆ (2004)299 PKA-camps FR◆ (2004)299 CAMYEL BR◓ (2007)300 CUTie FR◆ (2017)301 Epac-S: Epac-S H188 FR◆ (2015)302 Epac-S H187 FR◆ (2015)302 H74 FR◆ (2011)303 H96 FR◆ (2011)303 H90: CFP(nd)EPAC(ΔDEP/CD)-cp173Venus(d) FR◆ (2008)304 H84: CFP(nd)-EPAC(ΔDEP/CD)-Venus(d) FR◆ (2008)304 H81: GFP(nd)EPAC(ΔDEP)-mRFP FR◆ (2008)304 Epac1 (ΔDEP-CD) based: CEPAC* FR◆ (2011)305 CFP-Epac(ΔDEP-CD)-YFP FR◆ (2004)306 Flamindo: Pink Flamindo I☼ (2017)192 Flamindo2 I☼ (2014)189 Flamindo I☼ (2013)171 FPX: pPHT−PKA R◧ (2015)307 ICUE: RAB-ICUE I☼ (2018)211 ICUPID FR◆ (2011)308 ICUE-YR FR◆ (2011)308 ICUE3 FR◆ (2009)309 Rluc-EPAC-YFP BR◓ (2008)310 ICUE2 FR◆ (2008)311 ICUE1 FR◆ (2004)312 mCRIS based: cit-mCNBD-cer FR◆ (2013)313 mICNBD-FRET FR◆ (2016)314 Nano-lantern cAMP: Nano-lantern cAMP1.6 BL● (2012)290 R-FlincA I☼ (2018)315 R1α #7 FR◆ (2016)316 Split Luc cAMP biosensor: 22F BL● (2011)317 Split Luc cAMP biosensor BL● (2008)318 YFP-PKAc + CFP-PKAr: ΔPKA RIIb-CFP + PKAc-YFP FR◆ (2006)319 R2-Rluc + GFP-C BR◓ (2006)320 R1-Rluc + GFP-C BR◓ (2006)320 YFP-PKAc + CFP-PKAr(R230 K) FR◆ (2004)321 YFP-PKAc + CFP-PKAr FR◆ (2002)322 PKAc-S65T + PKArII-EBFP FR ◆ (2000)323

ATP (cont.)

FRET (FR◆) Inten. (I☼) Rat. (R◧)

cGMP

Ex. Rat. (EX★) FRET (FR◆) Inten. (I☼)

EX

★ (2013)293 Perceval

EX

★ (2014)291

★ (2009)294

EX

◆ (2008)295

FR

cGES: Cygnus I☼ (2010)324 cGES-DE5 FR◆ (2006)325 cGKI based: cGi family FR◆ (2007)326 cGull: Green-cGull I☼ (2017)172 CGY: CGY-del1 FR◆ (2000)327 cygnet: cygnet-2.1 FR◆ (2005)328 cygnet-2 FR◆ (2001)329 FlincG: H6-FGAM FR◆ (2013)330 H6-FGB I☼ (2013)330 δ-FlincG

★ (2008)173

EX

citrate

FRET (FR◆)

FLIP-Cit: FLIP-Cit-Y

carbon monoxide (CO)

Inten. (I☼)

COSer I☼ (2012)332

glucose

FRET (FR◆)

FLIPglu: FLII12Pglu-Y Series FR◆ (2008)333 FLIPglu-YΔ13 FR◆ (2006)334 FLIIXPglu-Y Series (X) series FR◆ (2005)335 FLIPglu-Y series FR◆ (2003)336 GIP: AcGFP1-GBPcys-mCherry FR◆ (2010)337 GIP C0Yi FR◆ (2008)338 GIP FR◆ (2003)339

glutamine

FRET (FR◆)

FLIPQ: FLIPQ-TV3.0

heme

FRET (FR◆) Inten. (I☼) Rat. (R◧)

CHY: CH49Y FR◆ (2017)341 CISDY: CISDY-9 FR◆ (2015)342 Fluorescence quenching Heme: HS1 R◧ (2016)343 HS1-M7A R◧ (2016)343 CG6 I☼ (2012)344

histidine

FRET (FR◆) Rat. (R◧)

FLIP-HisJ: FLIP-cpHisJ194 FHisJ R◧ (2017)999

insulin

Rat. (R◧)

RINS: RINS1 R◧ (2017)346

lactate

FRET (FR◆)

Laconic

maltose


FLIPmal: GFP2-MBP-RLuc2 BR◓ (2013)348 FLIPmal-YΔ1 MBP: PPYF-green I☼ (2011)202

FR

FR

◆ (2011)331

FR

FR

◆ (2005)335 FLIPglu-600u-Δ

◆ (2012)340

◆ (2009)345

FR

◆ (2013)347

11710

◆ (2008)283 FLIPmal-Y Series

FR

FR

◆ (2002)349


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readout mech

variants

cellular analytes maltose (cont.)

Inten. (I☼)

NAD+

FRET (FR◆) Inten. (I☼)

NAD+ Biosensor I☼ (2016)350 NAD-Snifit FR◆ (2018)351

NAD+/NADH ratio

Ex. Rat. (EX★) Inten. (I☼) Rat. (R◧)

T-Rex Based: RexYFP I☼ (2014)352 Peredox R◧ (2011)214 SoNar EX★ (2015)209

NADH

Inten. (I☼)

Frex: FrexH I☼ (2011)353 Frex I☼ (2011)353

NADP+

FRET (FR◆)

Apollo-NADP+ FR◆ (2016)354 NADPsor FR◆ (2016)355

NADPH

BRET (BR◓) Ex. Rat. (EX★)

iNap: iNap3 EX★ (2017)210 iNap2 EX★ (2017)210 iNap1 EX★ (2017)210 semisynthetic BRET sensor: SNAP-P30-[cpNLuc inserted]eDHFR BR◓ (2018)356

NADP+:NADPH ratio

FRET (FR◆)

NADP-Snifit

O2


dUnOHR hypoxia-reoxygenation sensor I☼ (2016)357 FluBO FR◆ (2012)358

phosphonate

Inten. (I☼)

EcPhnD based: EcPhnD-cpGFP I☼ (2011)203

pyruvate

FRET (FR◆)

PdhR based: PYRATES

ribose

FRET (FR◆)

FLIPrib: FLIPrib-Y family

sucrose

FRET (FR◆)

FLIPsuc: FLIPsuc-Y family

trehalose

FRET (FR◆)

FLIP: FLIPSuc90 μΔ1Venus

tryptophan

FRET (FR◆)

FLIPW

Biolum. (BL●) BRET (BR◓)

Ca2+ Snapshot: FLARE I☼ (2017)365 Cal-Light I☼ (2017)366 Cameleons: D3 BRET BR◓ (2015)367 D1GO-Cam FR◆ (2012)368 CaYang1 FR◆ (2011)369 CaYin1 FR◆ (2011)369 YC-Nano FR ◆ (2010)370 D3cpv FR◆ (2006)371 D4 cPV FR◆ (2006)371 D1 FR◆ (2004)372 YC3.60 FR◆ (2004)373 YC2.12 FR◆ (2002)374 YC6.1 FR◆ (2001)375 YC2.1 FR◆ (1999)376 Split-Cameleon FR◆ (1997)377 Cameleon 3 FR◆ (1997)377 Camgaroo: Camgaroo2 I☼ (2001)378 Camgaroo1 I☼ (1999)152

ions Ca2+

Em. Rat. (EM☆) Ex. Rat.(EX★) FRET (FR◆) Inten. (I☼) Rat. (R◧)

FR

FR

◆ (2018)351

◆ (2017)359 Pyronic

FR

FR

FR

◆ (2014)360

◆ (2003)361

FR

◆ (2006)362

FR

◆ (2016)363

◆ (2007)364

CaMPARI: CaMPARI I☼ (2015)379 CASE: Case16 I☼ (2007)380 Case12 I☼ (2007)380 CatchER I☼ (2011)148 ddFP Ca2+ sensor I☼ (2012)381 ER-GCaMP: ER-GCaMP6−150 I☼ (2017)382 ER-GCaMP6−210 I☼ (2017)382 ER-GCaMP3−373/GCaMPer-10.19 I☼ (2015)383 CEPIA1er I☼ (2014)384 ER-GECO: R-CEPIA1er I☼ (2014)384 GEM-CEPIA1er EM☆ (2014)384 G-CEPIA1er I☼ (2014)384 FPX Ca2+ sensors: tripartate FPX Ca2+ Sensor R◧ (2015)307 single polypeptide FPX Ca2+ sensor R◧ (2015)307 GAP: GAP3 I☼ (2016)385 GAP1 I☼ (2014)386 GCaMP: axon-GCaMP6 I☼ (2018)1000 ncpGCaMP6s I☼ (2018)387 MatryoshCaMP6s R◧ (2017)219 sfMatryoshCaMP6s R◧ (2017)219 sfMatryoshCaMP6s-T78H R◧ (2017)219 FGCaMP EX★ (2017)388 GCaMP-R3̅ R◧ (2017)215 GCaMP-R-6f R◧ (2017)215 GCaMP-R-6s R◧ (2017)215 GCaMP3bright I☼ (2015)389 GCaMP3fast I☼ (2015)389 GCaMP2.2Low I☼ (2014)390 GCaMP6f I☼ (2013)164 GCaMP6s I☼ (2013)164 GCaMP6m I☼ (2013)164 GCaMP5G I☼ (2012)163 G-CaMP 3−8 (Nakai Variants) I☼ (2012)391 GCaMP-HS I☼ (2011)392 GCaMP3 I☼ (2009)162 GCaMP2 I☼ (2006)161 GCaMP I☼ (2001)158 GECO: K-GECO1 I☼ (2018)393 LUCI-GECO1 BR◓ (2018)387 jRGECO1a I☼ (2016)170 REX-GECO EX★ (2014)208 LARGECO I☼ (2014)394 GR-GECO I☼ (2013)395 R-GECO1.2 I☼ (2013)167 O-GECO I☼ (2013)167 CAR-GECO I☼ (2013)167 GGECO1 I☼ (2011)166 B-GECO I☼ (2011)166 G-GECO1.2 I☼ (2011)166 R-GECO1 I☼ (2011)166 GEM-GECO EM☆ (2011)166 GEX-GECO EX★ (2011)166 G-GECO1.1 I☼ (2011)166 nanolantern Ca2+: CeNL(Ca2+)110 μ BR◓ (2018)396 CeNL(Ca2+)19 μ BR◓ (2018)396 GeNL(Ca2+) BR◓ (2016)397 ONL(Ca2+) BR◓ (2015)398 CNL(Ca2+) BR◓ (2015)398 nanolantern Ca2+BL● (2012)290 NTnC: YTnC I☼ (2018)399 iYTnC2 I☼ (2018)400 NTnC I☼ (2016)401 pericam: ratiometric-pericam R◧ (2001)156 inverse-pericam I☼ (2001)156 flash-pericam I☼ (2001)156

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variants

ions RCaMP: jRCaMP1b I☼ (2016)170 jRCaMP1a I☼ (2016)170 R-CaMP2 I☼ (2015)402 RCaMP1h I☼ (2013)168 R-CaMP1.07 I☼ (2012)403 TN Ca2+ Sensors: CalfluxVTN BR◓ (2016)404 Twitch series FR◆ (2014)405 RS-TN-XL FR◆ (2013)406 YO-TnC1.0 FR◆ (2012)407 TN-XXL FR◆ (2008)408 TN-XL FR◆ (2006)409 TN-humTnC FR◆ (2004)410 TN-L15 FR◆ (2004)410 Cd2+

FRET (FR◆)

Cd-FRET: Cd-FRET2

Cu+

FRET (FR◆)

yeast copper regulator based: Ace1-FRET

halide ions (Cl−, I−)

FRET (FR◆)

Clomeleon: SuperClomeleon


Dual pH and Cl sensor: ClopHensor R◧ (2010)142 halide sensitive fluorescent proteins: Cl-YFP I☼ (2014)133 YFP(H148Q, I152L) I☼ (2001)132 YFP(H148Q) I☼ (2000)130

Hg(II)

Inten. (I☼)

eGFP based: eGFP205C I☼ (2008)414 IFP based: IFP based Hg sensor I☼ (2011)415

Mg2+

FRET (FR◆)

MagFRET: MagFRET-1

phosphate

FRET (FR◆)

FLIPPi: FLIPPi-5μ

Zn2+

BRET (BR◓)

Atox1 WD4 based: BLCALWY-1 BR◓ (2016)418 redCALWY FR◆ (2013)419 eCALWY series FR◆ (2009)420 CALWY FR◆ (2007)421 CFP-Atox1 + WD4-YFP FR◆ (2006)422 GZnP: GZnP2 I☼ (2018)423 GZnP1 I☼ (2016)424 minimal zinc finger based: mito-ZifCY1.173 FR◆ (2012)425 His4-Zn2+ sensor FR◆ (2009)426 Cys2Hys2 - Zn2+ Sensor FR◆ (2009)426 Zap1 based: ZapCV5 FR◆ (2017)427 ZapCV2 FR◆ (2017)427 ZapCY2 FR◆ (2011)428 ZapCY1 FR◆ (2011)428 ZF1/2-FRET FR ◆ (2006)429 ZinCh: BLZinCh-2 BR◓ (2016)418 BLZinCh-3 BR◓ (2016)418 eZinCh-2 FR◆ (2015)147 eZinCh-1 FR◆ (2011)146 Cly9−2His FR ◆ (2008)430 ZinCh-x FR◆ (2007)145


FR

◆ (2011)146

FR

FR

FR

FR

◆ (2011)411 Mac1-FRET

◆ (2013)413 Cl-sensor

FR

FR

◆ (2011)411 Amt1-FRET

◆ (2008)140 Clomeleon

FR

FR

◆ (2010)412

◆ (2000)139

◆ (2013)416

◆ (2006)417 FLIPPi-30m

FR

◆ (2006)417

kinases/phosphatases Abl

FRET (FR◆)

Abl indicator

Akt

Ex. Rat. (EX★) FLIM (FL◇) FRET (FR◆) Trans. (T⇄)

Akind FR◆ (2006)432 Akt translocation Reporters: Akt-FoxO3a-KTR-EGFP T⇄ (2016)114 FoxO1-clover T⇄ (2015)113 AktAR: ExRai-AktAR EX★ (2018)211 AktAR2 FR◆ (2015)433 AktAR FR◆ (2008)434 AktUS FR◆ (2003)435 BKAR: BKAR v2 FR◆ (2014)436 BKAR FR◆ (2005)437 dual-labeled Akt: GFP-PKB-RFP FR◆ (2007)438 GFP-AKT-YFP FL◇ (2003)439 Eevee-Akt: Eevee-iAkt FR◆ (2014)440 Eevee-Akt FR◆ (2011)441 ReAktion: ReAktion1 FR◆ (2007)442

AMPK

FRET (FR◆)

AMPKAR: AMPKAR-EV

ATM kinase

FRET (FR◆)

ATOMIC

Aurora B kinase

FRET (FR◆)

Aurora B sensor: Aurora B sensor (Chu)

Aurora kinase A

FRET (FR◆)

AURKA Biosensor

B-Raf

FRET (FR◆)

Prin-BRaf

Bcr-Abl

FRET (FR◆)

Pickles: Pickles-2.3

C-Raf

FRET (FR◆)

Prin-CRaf

CaMKII

FLIM (FL◇)

Camui: Camuiα-mRmC FL◇ (2016)454 ShadowG-Camuiα FL◇ (2015)455 Camuiα-CR (2009)456 mRFP/GFP-Camuiα FL◇ (2008)457 Camuiα FR◆ (2005)458 K2α: RS-K2α FR◆ (2013)406 RY-K2α FR◆ (2013)406 YC-K2α FR◆ (2013)406

FRET (FR◆) CaN

FRET (FR◆)

FR

◆ (2001)431

FR

◆ (2017)443 bimABKAR

FR

◆ (2015)444 ABKAR

FR

FR

◆ (2015)445 AMPKAR

FR

◆ (2011)446

◆ (2007)447

FR

◆ (2011)448Aurora B Sensor (Fuller)

FR

◆ (2008)449

FR

◆ (2016)450

◆ (2006)451

FR

FR

◆ (2010)452

◆ (2005)453

FR

FR

◆ (2012)261 green-Camuiα

FL

◇

CaN activation: YC-CaN FR◆ (2013)406 RY-CaN FR◆ (2013)406 CaNAR: CaNAR2 FR◆ (2014)459 CaNAR1 FR◆ (2008)460 11712


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readout mech

variants

kinases/phosphatases CaNARi FR◆ (2014)459 DuoCaN: UniCaN FR◆ (2015)461 DuoCaN

FR

◆ (2015)461

CDK1

FRET (FR◆)

CyclinB1-Cdk1 activity sensor: CyclinB1-Cdk1 activity sensor V2 (2010)462

CDK2

Trans. (T⇄)

CDK2 translocation reporter: DHB-Ven T⇄ (2013)111

DAPK1

FRET (FR◆)

DAPK1

EGFR

FRET (FR◆)

ECaus FR◆ (2008)295 EGFR Reporter FR◆ (2001)431 FLAME: PTB-EYFP, EGFR-ECFP Picchu-Z FR◆ (2005)465

ERK

BRET (BR◓) FLIM (FL◇)

FLINC (FC▲) FRET (FR◆) Inten. (I☼) Rat. (R◧) Trans. (T⇄)

FR

FR

◆ (2014)436 CyclinB1-Cdk1 activity sensor V1

◆

FR

◆ (2011)463

◆ (2004)464 FLAME

FR

◆ (2004)464

FR

EAS: EAS3 FR◆ (2005)466 EKAR: VcpV FLARE EKAR FR◆ (2018)467 mCh-mCh FLARE EKAR FR◆ (2018)467 C3C3 FLARE EKAR FR◆ (2018)467 VV FLARE EKAR FR◆ (2018)467 RAB-EKARev I☼ (2018)211 EKARet FL◇ (2017)468 EKARFL◇ (2017)469 bimEKAR FR◆ (2015)444 EKAR3 FR◆ (2015)470 EKAREV-TVV FR◆ (2014)471 EKAR-TVV FR◆ (2014)471 REV BR◓ (2013)472 EKAR2G FR◆ (2013)473 EKAREV FR◆ (2011)441 EKAR FR◆ (2008)474 ERK-KTR T⇄ (2014)116 Erkus FR◆ (2007)475 ERKy FR◆ (2012)476 FLINC-EKAR1 FC▲ (2017)477 FPX EKAR R◧ (2015)307 Miu2 FR◆ (2006)478

FAK

FRET (FR◆)

FAK activation biosensor: CYFAK413 FR◆ (2008)479 FAK autophosphorylation biosensor FR◆ (2008)479 FAK sensor FR◆ (2011)480

Fus3

Trans. (T⇄)

Far1-SKARS T⇄ (2015)117

GCK

FRET (FR◆)

GCK activation biosensors: FRET-GCK (cp173-mCer3/mVen) FR◆ (2016)481 FRET-GCK (mCer/mVen) Cerulean-GCK-mCit FR◆ (2004)181 FRET-GCK FR◆ (2002)483

H3-S10p

FRET (FR◆)

H3S10p biosensor

H3-S28p

FRET (FR◆)

histone phosphorylation reporter

INSR

BRET (BR◓) FRET (FR◆) Inten. (I☼)

insulin receptor activation BRET assay BR◓ (2001)485 Phocus: Phocus-2pp nes FR◆ (2002)486 Phocus2 FR◆ (2002)486 Sinphos: yellow-sinphos I☼ (2004)487 green-sinphos I☼ (2004)487 cyan-sinphos I☼ (2004)487

JNK

FRET (FR◆) Trans. (T⇄)

dJUN-FRET FR◆ (2008)488 JNK-KTR T⇄ (2014)116 JNKAR: NIR JNKAR FR◆ (2018)489 bimJNKAR JuCKY FR◆ (2010)491

FR

Trans. (T⇄)

Ste7-SKARS T⇄ (2015)117

LATS

Biolum. (BL●)

LATS-BS

Lck

FRET (FR◆)

Lck activation sensor: CLckY-2

MAPK/MK2

FRET (FR◆)

GMB

MARK

FRET (FR◆)

MARK sensor: MARK-AR1

Mpk1

Trans. (T⇄)

Mkk2-SKARS T⇄ (2015)117

mTORC1

FRET (FR◆)

TORCAR

FR

◆ (2011)482

◆ (2018)1001

Kss1

BL

FR

FR

◆ (2004)484

◆ (2015)444 JNKAR1EV

FR

◆ (2011)441 JNKAR1

FR

FR

◆ (2010)490

● (2018)492 FR

◆ (2009)493

◆ (2001)494 FR

◆ (2011)495

◆ (2015)433

FR

11713


Chemical Reviews

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readout mech

variants

kinases/phosphatases p38


p38 activity reporter FR◆ (2015)496 p38-KTR T⇄ (2014)116

PAK1

FRET (FR◆)

Pakabi

PDGFR

FRET (FR◆)

PDGFR biosensor

PDK1

FRET (FR◆)

PARE

PKA

Biolum. (BL●)

AKAR: ExRai-AKAR EX★ (2018)211 blueAKAR I☼ (2018)211 RAB-AKARev I☼ (2018)211 sapphireAKAR I☼ (2018)211 VcpV FLARE AKAR FR◆ (2018)467 VV FLARE AKAR FR◆ (2018)467 C3C3 FLARE AKAR FR◆ (2018)467 C3 cPC3 FLARE AKAR FR ◆ (2018)467 mCh-mCh FLARE AKAR FR◆ (2018)467 NIR AKAR FR◆ (2018)489 AKARet FL◇ (2017)468 AKARdual FL◇ (2017)469 AKAR5 FL◇ (2017)500 AKAR4.1 FR◆ (2015)501 FLIM-AKAR FL◇ (2014)502 AKAR-CR FR◆ (2012)261 bimAKAR FR ◆ (2011)503 LumAKAR BL● (2011)503 AKAR3EV FR◆ (2011)441 AKAR4 FR◆ (2011)504 ICUPID FR◆ (2011)308 AKAR-GR FR ◆ (2011)308 CRY-AKAR FR◆ (2008)505 AKAR3 FR◆ (2006)506 AKAR2 FR◆ (2005)507 AKAR1 FR◆ (2001)508 ART FR◆ (2000)509 FLINC-AKAR1 FC▲ (2017)477 PKA-KTR T⇄ (2014)116 RLuc-PCA PKA BL● (2007)510 single color PKA sensor: GAk I☼ (2014)511

Ex. Rat. (EX★) FLIM (FL◇) FLINC (FC▲) FRET (FR◆) Inten. (I☼) Trans. (T⇄)

FR

◆ (2009)497 FR

◆ (2017)498

◆ (2011)499

FR

PKC

Ex. Rat. (EX★) FLIM (FL◇) FRET (FR◆) Inten. (I☼)

CKAR: ExRai-CKAR EX★ (2018)211 blueCKAR I☼ (2018)211 sapphireCKAR I☼ (2018)211 CKAR Eevee-PKC FR◆ (2011)441 IDOCKS: IDOCKSγ FL◇ (2018)513 IDOCKSβ FL◇ (2018)513 IDOCKSα FL◇ (2018)513 ITRACK: ITRACKγ FL◇ (2018)513 ITRACKβ FL◇ (2018)513 ITRACKα FL◇ (2018)513 KCP: KCAP-1 FR◆ (2006)514 KCP-2 FR◆ (2006)514 KCP-1 FR◆ (2004)515

PKD

FRET (FR◆)

DKAR FR◆ (2007)516 G-PKDrep: G-PKDrep live

FR

◆ (2012)517 G-PKDrep

FR

◆ (2003)512

◆ (2009)518

FR

PKM2

FRET (FR◆)

PKAR: PKAR2.3

FR

◆ (2013)519

PTEN

BRET (BR◓)

Rluc-PTEN-YFP

BR

◓ (2014)520

ROCK

FRET (FR◆)

Eevee-ROCK

RSK

FRET (FR◆)

Eevee-RSK

RTK

FRET (FR◆)

Picchu: PicchuEV

S6K

FRET (FR◆)

Eevee-S6K

SAP3K

FRET (FR◆)

SAP3K

Src

FRET (FR◆)

Src Indicator: BG-Src1.0 FR◆ (2013)524 YO-Src1.0 FR◆ (2012)407 Src Reporter (ECFP/YPet) FR◆ (2008)525 Src Reporter FR◆ (2005)526 Src Indicator FR◆ (2001)431 Srcus FR◆ (2007)527

ZAP-70

FRET (FR◆)

ROZA


5-HT 3A CNiFER: 5-HT 3A LGIC CNiFER dLight: 5HT2A-dLight I☼ (2018)204

ACh


ACh CNiFER: α4β2-nAChR LGIC CNiFER FR◆ (2011)529 α7 LGIC CNiFER FR◆ (2011)529 M1-CNiFER ACh-Snifit: ACh-SnifitWA-E FR◆ (2014)531 ACh-Snifit-E FR◆ (2014)531 ACh-Snifit-D FR◆ (2014)531 GACh: GACh2.0 I☼ (2018)206 GACh1.0 I☼ (2018)206

dopamine


dLight: dLight1.2 I☼ (2018)204 Dopamine CNiFER: D2 CNiFER FR◆ (2014)532 GRABDA: GRAB_DA1h I☼ (2018)205 GRAB_DA1m I☼ (2018)205

neurotransmitters 5-HT

FR

FR

FR

FR

◆ (2017)521

◆ (2011)441 ◆ (2011)441 Picchu

FR

◆ (2001)522

FR

◆ (2011)441

FR

◆ (2009)523

◆ (2008)528

11714

◆ (2011)529

FR

◆ (2010)530

FR


Chemical Reviews

Review

Table 1. continued target neurotransmitters GABA

readout mech

variants

FRET (FR◆)

GABA-Snifit


FLIP Glt: FLIP-cpGltI210 FR◆ (2009)345 FLIPE: FLII81E-1u FR◆ (2005)335 FLIPE-Y Surface series FR◆ (2005)534 FLIPE-Y Series GluSnFR: superGluSnFR FR◆ (2008)535 iGluSnFR: iGluu I☼ (2018)1002 iGluf I☼ (2018)1002 iGluSnFR I☼ (2013)174 QBP based: Gln D157N reporter FR◆ (2010)536 Snifit-iGluR5 FR◆ (2012)537

glycine

FRET (FR◆)

Glycine FRET sensor: GlyFS

FR

NE

FRET (FR◆)

NE CNiFER: α1A CNiFER

◆ (2014)532

glutamate

FR

◆ (2012)533

FR

◆ (2005)534

FR

◆ (2018)538

phosphoinositides/lipids 3′ IP


GFP AKT domains: GFP-Akt T⇄ (1999)91 GFP-AH T⇄ (1999)91 homo FRET mCherry-Akt-PH: mCherry-Akt-PH FR◆ (2015)539 InPAkt FR◆ (2005)540

DAG


Cys1-GFP: C12-GFP T⇄ (1998)98 Cys1-GFP T⇄ (1998)96 Daglas: Daglas-mit1 FR◆ (2006)541 Daglas-em1 FR◆ (2006)541 Daglas-pm1 DAGR FR◆ (2003)512 Digda FR◆ (2008)542

BRET (BR◓) FRET (FR◆) Trans. (T⇄)

FIRE: FIRE-3 FR◆ (2006)543 FIRE-2 FR◆ (2006)543 FIRE-1 FR◆ (2006)543 fretino: fretino BR◓ (2015)367 FRET InsP3 sensor FR◆ (2015)367 fretino-2 FR◆ (2005)544 GFP-PH: GFP-PHD T⇄ (1999)545 IRIS: IRIS-1 FR◆ (2006)546 LIBRA: LIBRAvIII FR◆ (2009)547 LIBRAVIIIS FR◆ (2009)547 LIBRAvII FR◆ (2009)547 LIBRAvI (2004)548

IP3

◆ (2006)541

FR

◆ (2009)547 LIBRA

FR

PA

FRET (FR◆)

Pii: Pii-DK

PI(4)P


BRET PI(4)P sensors: SidM-2xP4M Pippi: Pippi-PI(4)P FR◆ (2008)542

PI(3)P

Trans. (T⇄)

GFP-FYVE: GFP-FYVE (FENS-1) T⇄ (2001)94 GFP-Pib1p T⇄ (1998)90 GFP-EEA1 (FYVE) T⇄ (1998)90 GFP-PX T⇄ (2001)94

PI(3,4)P2

FRET (FR◆)

Pippi-PI(3,4)P2

PI(3,5)P2

Trans. (T⇄)

GFP-MLN1 T⇄ (2013)551

PI(4,5)P2

FRET (FR◆) Rat. (R◧) Trans. (T⇄)

CAY FR◆ (2004)552 CYPHR FR◆ (2003)512 FP-Tubby T⇄ (2001)553 FPX PIP2 sensor R◧ (2015)307 PH(PLCδ): PH(PLCδ)-CFP/YFP Pippi-PI(4,5)P2 FR◆ (2008)542

FR

FR

◆

◆ (2010)549 BR

◓ (2016)550 OSH2−2xPH

BR

◓ (2016)550

◆ (2006)432

FR

FR

◆ (2001)554 PH(PLCδ) - GFP T⇄ (1998)92 GFP-PH T⇄ (1998)555

PIP3

FRET (FR◆) Trans. (T⇄) BRET (BR◓)

FLLIP FR◆ (2003)556 labeled PH domains: PH(PKB)-GFP T⇄ (1999)93 PH(GRP1)-GFP T⇄ (1999)93 PIP3 BRET sensor BR◓ (2012)557

PS

Trans. (T⇄)

2xPH(Evectin2) T⇄ (2011)558 cPLA2-C2: cPLA2-C2-GFP T⇄ (2003)99 Lact-C2: mRFP - Lact-C2 T⇄ (2008)101 GFP - Lact-C2 T⇄ (2008)101 PKC-C2: PKCα-C2-EGFP T⇄ (2003)99 C2-GFP T⇄ (1998)98 PLCδ1-C2: PLCδ1-C2-EGFP T⇄ (2002)100

FRET (FR◆)

FRET-LC3B

proteases Atg4a

FR

◆ (2012)559

11715


Chemical Reviews

Review


readout mech

variants

proteases Atg4b

FRET (FR◆)

FRET-GATE-16

caspase1

Rat. (R◧)

FPX caspase 1 sensor: single polypeptide FPX caspase 1 sensor R◧ (2015)307

caspase3

FRET (FR◆)

ddFP based: single polypeptide FPX caspase 3 sensor R◧ (2015)307 bimolecular FPX caspase 3 reporter R◧ (2015)307 ddRFPA1B1-DEVD I☼ (2012)381 DEVD: mCitrine-DEVD-mTFP1 FR◆ (2008)560 mAmetrine-DEVD-tdTomato FR◆ (2008)560 BFP-DEVD-GFP FR◆ (2002)561 Sensor C3 FR◆ (2001)562 CFP-DEVD-YFP FR◆ (2000)563 DEVK: MiCy-DEVK-mKO FR◆ (2004)564 EC-RP FR◆ (2008)565 far-red caspase sensors: mKate2-DEVD-iRFP FR◆ (2016)566 Intein based: sDnaB-cpM4(DEVD) I☼ (2013)567 iProtease: iCasper I☼ (2015)568 SCAT3 FR◆ (2003)569 yDMQDc FR◆ (2006)570


FR

◆ (2012)559

caspase8

FRET (FR◆) Rat. (R◧)

Bid cleavage sensor FR◆ (2002)571 FPX caspase 8 sensor: single polypeptide FPX caspase 8 sensor R◧ (2015)307 IC-RP FR◆ (2008)565

caspase9

FRET (FR◆)

SCAT9

MT1-MMP

FRET (FR◆)

MT1-MMP Biosensor: MT1-MMP Biosensor (mCherry/mOrange2)

A1R

FRET (FR◆)

A1R SPASM: A1R - FL Gαs SPASM

β1-AR

Inten. (I☼)

dLight: β1AR-dLight I☼ (2018)204

β2-AR

BRET (BR◓) FRET (FR◆) Inten. (I☼) Trans. (T⇄)

BRET2 β2-AR activation Probes: β2AR-RLuc Gαs-GFP10 BR◓ (2005)575 β2AR-RLuc GFP-Gβ1 BR◓ (2005)575 β2AR-RLuc GFPGγ2 BR◓ (2005)575 dLight: β2AR-dLight I☼ (2018)204 β2-AR SPASM: β2-AR FL Gαq SPASM FR◆ (2017)574 β2-AR FL Gαs SPASM FR◆ (2017)574 β2-AR-Gα SPASM FR◆ (2013)576 Nb80: Nb80-GFP T⇄ (2013)109

Dictyostelium GPCR

FRET (FR◆)

labeled G proteins: Gβ-YFP + Gα2-CFP

DRD2

Inten. (I☼)

iTango: DRD2-iTango I☼ (2017)578

Gαi

FRET (FR◆)

Gαi sensor: Gαi1 sensor v2 FR◆ (2016)579 Gαi2 Sensor v2 FR◆ (2016)579 Gαi3 sensor v2 FR◆ (2016)579 Gαi1 v1 (2006)580 Gαi2 v1 FR◆ (2006)580 Gαi3 v1 FR◆ (2006)580 Bunemann Gαi-Gγ2 FR◆ (2003)581 Bunemann Gαi-Gβ1 (2003)581

Gαq

FRET (FR◆)

Gαq sensor: Gαq sensor (v2)

Gαs

BRET (BR◓) FRET (FR◆) Trans. (T⇄)

Gαs sensor FR◆ (2006)584 Gs activation BRET assay: RLucII-117-Gαs + GFP10-Gγ1 Nb37 based: Nb37-YFP T⇄ (2017)110

α1-AR

FRET (FR◆)

α1-AR SPASM: α1-AR FL Gαq SPASM

α2A-AR

BRET (BR◓) FRET (FR◆) Inten. (I☼)

dLight: α2AR-dLight I☼ (2018)204 α2-AR SPASM: α2-AR FL Gαs SPASM FR◆ (2017)574 α2-AR FL Gαi SPASM FR◆ (2017)574 α2A-AR + labeled G protein: α2A-AR-Venus + Gαi1-122Rluc BR◓ (2006)586 α2A-AR-Venus + Gαi1-91Rluc BR◓ (2006)586 α2AAR-Venus + RLuc-Gγ2 BR◓ (2006)586 α2A-YFP + CFP-Gγ2 FR◆ (2005)587 α2A-AR activation sensor: α2A-AR-cam FR◆ (2003)588

M1R

FRET (FR◆)

M1R activation sensor: M1R-EYFP-Cerulean

MT2

Inten. (I☼)

dLight: MT2-dLight I☼ (2018)204

Odr-10

BRET (BR◓)

OGOR

FR

◆ (2003)569 ◆ (2010)572 MT1-MMP Biosensor

FR

◆ (2008)573

FR

receptors/GPCRs

BR

FR

FR

◆ (2017)574 A1R - FL Gαi SPASM

FR

◆ (2001)577

◆ (2011)582 Gαq Sensor (v1)

FR

◆ (2017)574

FR

BR

FR

◆ ◆

FR

FR

◆ (2009)583

◓ (2016)585

◆ (2017)574

FR

◆ (2009)583

◓ (2011)589

11716


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readout mech

variants

receptors/GPCRs κ-opioid

Inten. (I☼)

dLight: KOR-dLight I☼ (2018)204

μ-opioid

Inten. (I☼)

dLight: MOR-dLight I☼ (2018)204

Opsin

FRET (FR◆)

Opsin SPASM: Opsin-Gα SPASM

PTHR

FRET (FR◆)

PTHR activation sensor: PTHR-cam

TrkB

FLIM (FL◇)

TrkB-PLC interaction: TrkB sensor

VEGF

BRET (BR◓)

BRET VEGF biosensor

Ex. Rat. (EX★) FRET (FR◆)

HyPer: HyPerRed EX★ (2014)592 HyPer-3 EX★ (2013)593 HyPer-2 EX★ (2011)594 HyPer EX★ (2006)595 roGFP based: roGFP2-Tsa2ΔCPΔCR EX★ (2016)596 roGFP2-Tsa2 ΔCR EX★ (2016)596 roGFP2-GPx4 EX★ (2009)597 roGFP2Orp1 EX★ (2009)597 unnatural amino acid based: UFP-Tyr66pBoPhe I☼ (2012)598 Yap1 based: PerFRET FR ◆ (2013)599 OxyFRET FR◆ (2013)599

redox H2O2

Inten. (I☼)

BR

◆ (2013)576

FR

FR

FL

◆ (2003)588

◇ (2016)590

◓ (2016)591

H2S

Inten. (I☼)

pAzF H2S sensors: hsGFP I☼ (2014)600 cpGFP-Tyr66pAzF I☼ (2012)601

NO

FRET (FR◆)

FRET-MT FR◆ (2000)602 sGC based: Piccell FR◆ (2006)603 NOA-1

FR

◆ (2005)604

organic hydroperoxides

Inten. (I☼)

OHSer I☼ (2010)605

ONOO−

Inten. (I☼)

pnGFP I☼ (2013)606

redox status

Ex. Rat. (EX★) FRET (FR◆) Inten. (I☼)

HSP33: HSP-FRET FR◆ (2006)607 redox-sensitive linker: CY-RL7 FR◆ (2011)608 RedoxFluor FR◆ (2010)609 ECFP-RL-EYFP FR◆ (2008)610 roGFP: roGFP1-iX EX★ (2008)611 Grx1-roGFP2 EX★ (2008)612 roGFP1-Rx family EX★ (2006)613 roGFP2 EX★ (2004)143 roGFP1 EX★ (2004)143 rxRFP: TrxRFP1 I☼ (2017)614 rxRFP1.X sensitivity series I☼ (2016)615 rxRFP I☼ (2015)616 rxYFP: rxYFP-Grx1p I☼ (2006)617 rxYFP149202 I☼ (2001)618

superoxide

Inten. (I☼)

mt-cpYFP I☼ (2008)619

FLIM (FL◇) FRET (FR◆)

(CDC42) GEF sensor FR◆ (2003)620 CDC42 biosensor FR◆ (2014)621 Cdc42 FRET: Cdc42-CyRM FL◇ (2016)622 Cdc42 FRET (mRuby2/mCherry (I202Y)) (2011)623 CDC42 Raichu: CDC42 Raichu FR◆ (2002)624 CRIB Raichu FR◆ (2002)624 Cdc42−2G: Cdc42−2G FR◆ (2016)625 GDI Cdc42 FLARE FR◆ (2016)626

small G-proteins Cdc42

FL

◇ (2016)454 Cdc42 FRET

◇

FL

Rab5

FRET (FR◆)

Rab5 Raichu

◆ (2008)627

Rac1

FRET (FR◆)

FLAIR FR◆ (2000)628 FLARE: Rac1-FLARE FR◆ (2009)629 GDI Rac1 FLARE FR◆ (2016)626 Rac1 Raichu: Rac1 Raichu EV FR◆ (2011)441 CRIB Raichu FR◆ (2002)624 Rac1 Raichu FR◆ (2002)624 single chain Rac1 biosensors: NIR Rac1 biosensor FR◆ (2018)489 single chain Rac1 biosensor v2 FR◆ (2016)630 Rac1-2G (2015)631 single-chain Rac1 biosensor FR◆ (2014)632

FR

Rac2

FRET (FR◆)

single chain Rac2 biosensor

FR

◆ (2016)630

Rac3

FRET (FR◆)

single chain Rac3 biosensor

FR

◆ (2017)633

Ral

FRET (FR◆)

Raichu-Ral: Raichu-RalB

Ran

FRET (FR◆)

Ran FRET probes: YIC

FR

◆ (2004)634 Raichu-RalA

◆ (2002)635 YRC

FR

11717

FR

FR

FR

◆

◆ (2004)634

◆ (2002)635 DOI: 10.1021/acs.chemrev.8b00333 Chem. Rev. 2018, 118, 11707−11794

Chemical Reviews

Review


readout mech

variants

small G-proteins Rap1

FRET (FR◆)

Rap1 Raichu

Ras


DORA Ras FR◆ (2015)637 FRas: ShadowY H-Ras sensor FL◇ (2017)638 ShadowG FRas2-M FL◇ (2015)455 FRas2-M (2013)639 FRas-F FL◇ (2006)640 FRas FL◇ (2006)640 Raichu: Ras Raichu EV FR◆ (2011)441 Ras Raichu FR◆ (2001)636

RhoA


GDI FLARE: GDI-RhoA FLARE FR◆ (2016)626 FLARE: RhoA DORA FR◆ (2015)641 RhoA2G FR◆ (2013)473 bimolecular RhoA Biosensor FR◆ (2009)629 RhoA-FLARE/ RhoA1G FR◆ (2006)642 RhoA FRET: RhoA-CyRM FL◇ (2016)622 RhoA FRET (mRuby2/mCherry (I202Y)) FL◇ (2016)454 RhoA-FRET FL◇ (2011)623 Raichu: RhoA Raichu CR FR◆ (2012)261 RBD Raichu FR◆ (2006)643 RhoA Raichu FR◆ (2003)644

RhoB

FRET (FR◆)

DORA RhoB: RhoB FRET sensor

RhoC

FRET (FR◆)

DORA RhoC: RhoC FRET Sensor RhoC FLARE FR◆ (2013)646

RhoQ

FRET (FR◆)

TC10 Raichu

RRas

FRET (FR◆)

RRas Raichu

FR

◆ (2001)636

FR

FR

FR

FL

◇ (2013)639 FRas2-F

FL

◇

◆ (2016)645

FR

◆ (2016)645

◆ (2006)647

◆ (2007)648

other post-translational modifications βArrestin 2

BRET (BR◓)

βArrestin 2 ubiquitination biosensor

histone acetylation

FRET (FR◆)

Histac: Histac-H3K9/14

K27H3 methyltransf.

FRET (FR◆)

K27 reporter

K9H3 methyltransf.

FRET (FR◆)

K9 reporter: H3K9me3 Biosensor

O-GlcNAc transferase

FRET (FR◆)

O-GlcNAc sensor

ubiquitination

Inten. (I☼)

REACh-Ubiquitin I☼ (2006)655

FR

FR

BR

◓ (2004)649

◆ (2016)650 Histac-H4K12

FR

◆ (2011)651 Histac-H4K5/8

◆ (2009)652

FR

◆ (2004)653

◆ (2018)1001 K9 reporter

FR

◆ (2004)653

FR

◆ (2006)654

FR

other signaling proteins Annexin 4

FRET (FR◆)

NEX4: ORNEX4

Bax

Trans. (T⇄)

Bax translocation reporter T⇄ (2016)566

CaM

FRET (FR◆)

BSCaM: BSCaM2 FR◆ (1999)658 BSCaM1 MLCK-FIP FR◆ (2002)660

CRAC

Trans. (T⇄)

PH(crac)-GFP T⇄ (2002)661

CREB

FRET (FR◆)

ICAP

MLKL

FRET (FR◆)

SMART: hSMART

N-WASP

FRET (FR◆)

N-WASP BS

plasma membrane ATPase

FRET (FR◆)

PMCA Activity Sensor: BFP-PMCA-GFP

FR

FR

◆ (2008)656 CYNEX4

FR

◆ (2006)657

◆ (1997)659

FR

◆ (2010)662

FR

◆ (2018)1003 mSMART

FR

FR

◆ (2018)1003

◆ (2004)663 FR

◆ (2007)664

a

For each signaling target, the different published biosensors are organized into families of related variants (with the family or group name shown in bold). The different read-out mechanisms utilized are shown with the different icons indicating the type of readout for a given biosensor. The different readout mechanisms shown in this table are bioluminescence intensity (BL●), BRET (BR◓), intensity-based FRET (FR◆), FLIM-FRET (FL◇), FLINC (FC▲), intensity (I☼), ratiometric (with a reference FP) (R◧), excitation ratiometric (EX★), emission ratiometric (EM☆), and translocation (T⇄). Additional details, including biosensor components and FPs involved, etc., can be found in the supplemental materials and at BiosensorDB.ucsd.edu. 11718


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fluorescence behavior. Tinkering with the precise nature of this coupling (i.e., how the sensing unit affects fluorescence) lies at the heart of biosensor design. Indeed, as will be seen below, how FPs are incorporated into biosensors greatly influences the types of signaling activities that can be detected.

number of groups have also utilized homology-independent repair pathways for efficient FP tagging in both dividing and nondividing cells.35,36 On the other hand, given that HDR efficiency is also influenced by insert size, Kamiyama and colleagues utilized a previously developed split-GFP, in which GFP is divided between the 10th and 11th β-strands into two spontaneously complementing fragments,37 as an alternative strategy to improve tagging efficiency.38 Here, the target protein is endogenously tagged with the GFP11 “epitope” and becomes fluorescent upon complementation by exogenously expressed GFP1−10. The insertion of tandem epitope arrays can further be used to amplify the fluorescence signal from proteins with low endogenous expression.38 Recent advances in the development and application of a variety of scaffolds for generating intracellular affinity reagents (e.g., intracellular antibodies or “intrabodies”, reviewed in ref 39) offer another exciting approach for monitoring endogenous protein dynamics that serves as a potential alternative to genome editing. These include “monobodies” derived from the 10th domain of human type III fibronectin (10FnIII or Fn3), a well-characterized scaffold that is structurally similar to the immunoglobulin VH domain,40,41 as well as “nanobodies” derived from the heavy chain-only antibodies present in members of the Camelidae family (e.g., camels, llamas, alpacas) and in Chondrichthyes (e.g., sharks), wherein antigen recognition is mediated by a single variable domain.39,42 These small, soluble domains are easily expressed in living cells and, when fused to FPs, represent powerful, genetically encodable tools for the sensitive and specific visualization of endogenous cellular components such as neuronal proteins (PSD-95 and Gephyrin) at excitatory and inhibitory synapses,43 endogenous actin dynamics,44 Wnt-induced βcatenin translocation,45 PARP1 translocation to sites of DNA damage,46 and GTP-bound (i.e., active) H-Ras and K-Ras.47 As with any biosensor, interactions between intrabodies and endogenous cellular components may alter or otherwise perturb normal function, and rigorous controls are therefore needed to ensure that downstream processes are not adversely affected. In addition, the heterologous nature of this approach means that care must be taken to minimize background fluorescence caused by excess, unbound probe, with many studies utilizing stable cell lines to achieve low nanobody expression,45,46,48,49 while Gross and colleagues notably employed a transcriptional regulation system to link monobody expression to target protein levels.43 A recently described “flashbody” design also promises to help increase signal-tonoise ratio (SNR) by coupling antigen binding to the intensity of a circularly permuted FP (cpFP; discussed further in section 2.3.3.1).50 Thus, the legacy of fluorescence as a cell biological tool appears to have come full circle, with intrabodies essentially marking the emergence of “native” immunofluorescence. 2.2.2. Protein−Protein Interactions. Proteins rarely operate in isolation; most in fact participate in myriad transient interactions with other proteins, with many functioning as dimers or higher-order oligomers or assembling into multiprotein complexes that behave as dedicated molecular machines. Thus, in addition to marking the presence and location of proteins within a cell, direct FP tagging can also be used to monitor protein−protein interactions (PPIs) by utilizing fluorescence (or Förster) resonance energy transfer (FRET), a photophysical phenomenon wherein an excited “donor” fluorophore (e.g., an FP) nonradiatively transfers its

2.2. Biosensors for Monitoring Protein Behavior

2.2.1. Protein Expression, Turnover, And Localization. In keeping with the tradition of using fluorescence as a sensitive and specific in situ label, the simplest biosensor designs utilize FPs as passive, genetically encoded reporters for monitoring the dynamics of gene and protein expression, as well as protein turnover and localization, in living cells. Although generally involving little more than fusing an FPcoding sequence directly to a target DNA sequence, this approach is frequently modified to increase the richness of biological information that can be obtained. For example, despite serving as a ready indicator for the presence or absence of gene expression under steady-state conditions, the stability and long-lived fluorescence of FPs (discussed in ref 11) can nevertheless obscure the detection of endogenous transcriptional dynamics. One solution has been to incorporate destabilizing elements into the FP sequence.12−14 A variety of fluorescent “timers”, whose distinct chromophore maturation rates give rise to time-dependent changes in their fluorescence spectra, have also been developed to differentially label newly synthesized genes or proteins,15−19 permitting the chronological mapping of numerous dynamic processes, including transcriptional activity;16,20 protein trafficking, dynamics, and degradation;15,17 and even the intracellular replication and spread of influenza.21 Similarly, photoconvertible FPs, where the fluorescence emission spectra can be irreversibly converted to a different wavelength, have been utilized to quantify protein degradation in a manner that is not impacted by production of new FPs.22−24 Meanwhile, photoswitchable FPs25 are increasingly used as genetically encoded protein tags in conjunction with live-cell superresolution imaging techniques,26,27 thereby enabling the precise and dynamic visualization of protein localization in living cells. However, the heterologous overexpression of FP-tagged chimeras can potentially disrupt endogenous cellular processes and induce artifacts related to protein localization and dynamics. As such, researchers are increasingly exploring methods for monitoring the dynamics of proteins expressed at endogenous levels from their native genomic loci. Notable among these approaches has been the recent rise of genome editing techniques, particularly CRISPR/Cas technology (reviewed in detail by refs 28−30). In general, these techniques are based on the sequence-specific introduction of DNA double-strand breaks that are subsequently repaired via nonhomologous end-joining (NHEJ) or homology-directed repair (HDR). Furthermore, whereas NHEJ is typically error prone, HDR enables the precise, site-specific introduction of exogenous sequences, and numerous recent studies have successfully utilized CRISPR/Cas technology to incorporate FPs at endogenous loci in living cells31−33 and even in vivo.34 Nevertheless, the low-efficiency of HDR-mediated genome editing poses a major obstacle to the wider adoption of endogenous protein tagging, as does the lack of HDR in nondividing cells. Mikuni et al. cleverly skirted the latter issue by targeting embryonic neuronal precursors to achieve endogenous protein tagging via HDR in the brain,34 while a 11719


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excited state energy to a nearby “acceptor” fluorophore via dipole−dipole interactions (reviewed extensively in refs 11 and 51), yielding an increase in “sensitized” fluorescence emission by the acceptor (i.e., acceptor emission upon donor excitation) at the expense of direct emission by the donor. FRET is critically dependent on the proximity of the donor and acceptor fluorophores. Specifically, FRET efficiency (E) varies with the inverse sixth power of the distance, as defined by the equation É ÅÄÅ 6 Ñ−1 ÅÅ ij r yz ÑÑÑÑ Å j z E = ÅÅÅ1 + jj zz ÑÑÑ j R 0 z ÑÑ ÅÅ k { ÑÖ ÅÇ

essentially become trapped, rendering BiFC ill-suited to the study of dynamic PPIs. Nevertheless, the large dynamic range and irreversible nature of BiFC is in fact a tremendous boon for the detection of weak or transient PPIs. These properties also make BiFC a good fit for high-throughput approaches, and BiFC is widely used for PPI screening, similar to the yeast twohybrid (Y2H) assay, having recently been used in conjunction with Y2H screening to map PPIs related to 2-component phospho-relays in rice76 and to map PPIs in the pathogenic yeast Candida albicans.77 When combined with photochromic FPs, BiFC can also be utilized to localize protein interaction pairs in super-resolution, as was done recently to map the interaction between the E. coli proteins MreB and EF-Tu,78 to localize dimers of the microtubule plus-end protein EB1,79 and to visualize HER2-HER3 and STIM1-ORAI1 interactions in living cells.80 Although the latter study successfully visualized changes in STIM-ORAI complex formation in response to endoplasmic reticulum (ER) calcium (Ca2+) depletion, FRET remains the preferred method for monitoring dynamic PPIs. For instance, Jean-Alphonse and colleagues used FRET to monitor dynamic changes in the interactions among β-arrestin (βarr) 2, Gβγ, adenylyl cyclase (AC) 2, and the parathyroid hormone receptor (PTHR) in response to agonist stimulation,81 while a recent study by Smith et al. used FRET to monitor the interaction between the catalytic and regulatory subunits of 3′,5′-cyclic adenosine monophosphate (cAMP)dependent protein kinase (PKA) and argue against dissociation of the PKA holoenzyme under physiological conditions82 (discussed further in section 4.3).

(1)

where r is distance between the donor and acceptor and R0, known as the Förster distance, is the characteristic distance at which a given donor/acceptor pair exhibits half-maximal FRET efficiency, which is influenced by photophysical factors such as the degree of overlap between the emission spectrum of the donor and the absorption spectrum of the acceptor, as well as by the spatial alignment of the donor and acceptor dipoles. This proximity dependence allows FRET to serve as an exquisite molecular ruler52,53 that is routinely used to monitor PPIs in living cells, including in recent studies visualizing direct interactions between rate-limiting glucose metabolic enzymes in the formation of multiprotein “glucosomes”54 and between HIV proteins,55,56 as well as the formation of heteromeric potassium channels 57 and A-kinase anchoring protein (AKAP)-mediated signaling complexes.58−63 In addition to FRET, PPIs can also be detected in live cells through the use of protein-fragment complementation assays (PCAs). PCAs are an adaptation of classic studies in which fragments of enzymes such as β-galactosidase were found to spontaneously associate in vitro to regenerate an intact, active enzyme,64 with the major distinction being the use of protein fragments that do not spontaneously associate but instead can be induced to reassemble and reconstitute a functional unit when brought into close proximity by a pair of interacting proteins. Concurrent with the development of PCAs based on various enzymes (β-galactosidase, dihydrofolate reductase, βlactamase),65−68 FPs were also found to withstand dissection into roughly 157- and 81-residue fragments that can reassemble to produce a fluorescent species with the aid of a protein interaction pair,69,70 yielding the widely used biomolecular fluorescence complementation (BiFC) assay (reviewed in refs 71, 72). A novel, trimolecular fluorescence complementation (TriFC) assay has also been reported featuring a modification of the aforementioned split-FP approach, in which both the 10th and 11th β-strands are dissected out of GFP and individually tagged to proteins of interest.73 When brought into close proximity via PPIs, the two strands will reassemble with the separately expressed GFP1−9 sensor domain, thereby generating a fluorescent signal. This approach improves the expression and solubility of the tagged proteins by reducing the bulk of the fused FP fragments and was recently utilized to map PPIs among proteins related to frontotemporal dementia.74 Despite numerous advances in FP engineering, the relatively slow kinetics of FP refolding and chromophore maturation, which exhibit half-times ranging from minutes to hours depending on the specific variant,75 can limit the temporal resolution of BiFC. FP fragment complementation is also largely irreversible, such that protein interaction partners

2.3. Biosensors for Monitoring Biochemical Activity Dynamics

As discussed above, the simplest implementation of a genetically encoded fluorescent biosensor is to use a gene or protein of interest as a proxy for itself. Over the years, however, more sophisticated approaches have been devised to leverage the fact that proteins dynamically alter their behavior in response to a multitude of biochemical inputs. Thus, through the considered use of various proteins or protein modules conjugated to one or more FPs, genetically encoded fluorescent biosensors can be designed that variously change their localization, fluorescence intensity, or other spectral properties in response to, and thereby report on, fluctuations in a specific biochemical parameter within the native cellular environment. As such, it has become possible to directly visualize and probe the constant biochemical flux that defines a living cell. 2.3.1. Inducing the Translocation of a Fluorescent Protein. The distribution of proteins within cells is highly dynamic, with proteins often changing their subcellular localization in response to various biochemical signals. As such, the ability of FP tags to report on protein localization can logically be repurposed to utilize the signal-induced change in protein localization as a proxy for the signal itself. Indeed, such translocation-based biosensors were among the earliest genetically encoded probes developed to monitor dynamic biochemical activities in living cells. This approach has classically been used to monitor the production and distribution of lipid messengers such as phosphoinositides (i.e., inositol phospholipids), which comprise a minute but critical component of cell membranes and are responsible for mediating numerous cellular processes.83 Phosphoinositide signaling occurs through the direct recruit11720


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ment of effector proteins to the plasma membrane, which is mediated by different lipid-binding modules,84−86 including pleckstrin homology (PH) domains,87 Epsin N-terminal homology domains,88 and FYVE domains (named for the proteins Fab1p, YOPB, Vps27p, and EEA1).89 Furthermore, because lipid-binding domains from different effectors have been shown to selectively bind specific phosphoinositides (discussed in depth in ref 86), such as PI(4,5)P2 by the PH domain from phospholipase C (PLC)δ, PI(3,4,5)P3 by the PH domain of Btk, or PI(4,5)P2/PI(3,4,5)P3 by the PH domain from protein kinase B (PKB)/Akt, a certain degree of selectivity can in theory be achieved based on the chosen binding module, though appropriate controls should nevertheless be employed given that lipid selectivity is typically assessed in vitro. Lipid sensors are thus constructed by directly fusing the coding sequence of an FP to that of a full-length effector protein90,91 or an isolated lipid-binding module,90,92−94 whereby the translocation of the fluorescence signal to and/ or from a membrane structure serves as a clear indicator for the visualization of phosphoinositide dynamics (Figure 1A). The application of this type of biosensor has provided numerous insights into the function and regulation of phosphoinositide signaling, particularly with respect to the spatial organization of 3′-phosphoinositides during chemotaxis and cell migration (discussed further in section 4.2.3), while also remaining a straightforward and powerful approach that continues to be utilized to study the molecular details of phosphoinositide signaling. For example, in their study investigating neutrophil migration in live zebrafish embryos, Yoo and colleagues used a translocation-based biosensor in which the PH-domain of Akt is fused to GFP to visualize plasma membrane PI(4,5)P2/ PI(3,4,5)P3 accumulation, and thus phosphoinositide 3-kinase (PI3K) activation, at the leading edge in neutrophils migrating toward laser-induced wounds in vivo.95 This approach has also been used to detect other lipids in various membrane compartments. For instance, diacylglycerol (DAG), a plasma membrane lipid product formed by the PLC-catalyzed cleavage of PI(4,5)P2, can be detected using the C1 domain of protein kinase C (PKC),96−98 whereas phosphatidylserine can be recognized using C2 domains derived from either PKC,98,99 PLCδ,100 or the milk glycoprotein lactahedrin.101 However, the overall generalizability of this biosensor design strategy is somewhat restricted given the limited availability of endogenous binding modules that are capable of being recruited by specific intracellular targets to drive probe translocation. In this regard, nanobodies have emerged as a promising new resource for the construction of translocationbased sensors. In particular, their compactness renders nanobodies amenable to high-throughput library screening,102 thereby facilitating the rapid development of novel nanobodies, and the diversity of potential epitopes that can be recognized means that a much wider range of targets can be detected compared with more traditional translocation-based sensors. For example, Rajan et al. recently generated a nanobody to detect endogenous histone H2AX phosphorylated at serine 139 (γ-H2AX), which is frequently used as an intracellular marker of DNA double-strand breaks, and used the resulting GFP-tagged chromobody to directly visualize the endogenous formation of DNA double-strand breaks in living cells.103 Specifically, by inducing DNA breaks using laser microirradiation, the authors were able to observe the translocation of the γ-H2AX chromobody to break sites. Nanobodies can

Figure 1. Translocation-based fluorescent biosensors. (A) PH domains from different proteins are fused to an FP and translocate to the plasma membrane upon the production of specific phosphoinositides.92,93 For example, phosphorylation of PIP2 by PI3K to produce PIP3 at the plasma membrane causes translocation 11721


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excited-state proton transfer (ESPT).118,119 ESPT is driven by an extensive H-bond network that functions as a proton relay to abstract a high-energy proton from the excited chromophore while alternately serving to stabilize the neutral (i.e., protonated) chromophore in the ground state. As a result, wtGFP fluorescence is largely insulated from external perturbations.120,121 Yet many spectrum-shifting GFP mutations (e.g., S65T, Y66H, T203I),5,119,122,123 which disrupt the wild-type H-bond network, were shown to impart marked pH sensitivity to GFP fluorescence, responding rapidly and dynamically to intracellular pH changes and suggesting that GFP may be capable of serving as a genetically encoded pH indicator in live cells.124 This approach represents a conceptual departure from the biosensor designs mentioned in the preceding sections, wherein the FP largely serves as a passive bystander, by instead directly integrating the FP into the detection scheme and making probe fluorescence actively responsive to the detection of the target cellular parameter (e.g., pH), analogous to the behavior of small-molecule fluorescent indicators.3 Indeed, Llopis and colleagues were able to utilize different GFP spectral variants (e.g., EGFP, ECFP, and EYFP) with differing pKa values (6.15, 6.4, and 7.1, respectively) to visualize pH changes in HeLa cells, further taking advantage of the subcellular targetability of these genetically encoded probes to specifically monitor pH dynamics within the cytoplasm, Golgi lumen, and mitochondrial matrix.125 Miesenböck and colleagues also set out to specifically engineer a GFP-based pH sensor to monitor synaptic vesicle exocytosis by targeting residues known to participate in the H-bond network with Y66 or to otherwise alter the spectral properties of GFP.121 The authors scanned these sites with histidines, which had the desired pKa value to detect the pH transition upon vesicle fusion and also used random mutagenesis to ultimately obtain a “pH-sensitive fluorescent protein” (pHluorin), whose fluorescence intensity was eclipsed at low pH (i.e., “ecliptic” pHluorin). Ecliptic pHluorin is thus dark when localized to the vesicle lumen and rapidly increases in intensity upon exposure to the extracellular space, thereby enabling the visualization of individual fusion events as discrete flashes.121 Sankaranarayanan et al. subsequently generated a superecliptic pHluorin variant by incorporating the EGFP mutations F64L and S65T,126 while Li and Tsien recently developed a redfluorescent pH sensor, pHTomato, which they coimaged alongside GCaMP3 to simultaneously visualize synaptic vesicle and Ca2+ dynamics.127 Of the spectral variants initially derived from GFP, YFP fluorescence intensity displays a particularly high degree of environmental sensitivity, with the numerous amino acid substitutions having introduced gaps around the chromophore and the further substitution of H148 with Gly even producing a solvent channel directly to the chromophore, which substantially increases the already high pKa value (i.e., pH sensitivity) of YFP.128 In fact, Tojima and colleagues utilized this H148G substitution to reintroduce pH sensitivity into Venus for use as a pH sensor.129 YFP fluorescence is also strongly affected by halide ions, especially Cl− and iodide, with the H148Q mutant being particularly sensitive.130,131 Halide ions are able to bind directly to YFP and increase the apparent pKa of the chromophore, leading to decreased YFP intensity at a fixed pH, thus allowing Jayaraman and co-workers to use YFP(H148Q) as an intracellular Cl− sensor.130 Galietta and colleagues later set out to improve the halide sensitivity of

Figure 1. continued of the biosensor from the cytosol to the plasma membrane. (B) Kinase translocation reporters utilize kinase-specific substrate sequences within nuclear localization sequences (NLS) and/or nuclear export sequences (NES) to promote nuclear import when dephosphorylated and nuclear export when phosphorylated.114,116

also be generated that selectively recognize specific protein conformations. Indeed, several nanobodies have been developed that specifically bind to the active forms of Gprotein-coupled receptor (GPCR) signaling pathways components, including the β2-adrenergic receptor (β2-AR) (Nb80104 and Nb6B9105), the muscarinic acetylcholine receptor (Nb9− 8106), the μ opioid receptor (Nb39107), and the Gαs heterotrimeric G protein subunit,108 some of which have already been utilized as translocation-based sensors to probe GPCR signaling in living cells (see, for example, section 4.1 below).109,110 In contrast to the above examples, in which the sensing unit intrinsically relocalizes to the site of the target signal, a slightly different implementation of this approach involves triggering the relocalization of a sensor to a predetermined subcellular site. For example, Spencer and colleagues were able to develop a biosensor that exits the nucleus in response to CDK2 activity111 by fusing Venus (YFP) to the C-terminal domain of human DNA helicase B (HDHB), which mediates the cell cycle-dependent nuclear localization of HDHB in response to CDK2-dependent phosphorylation.112 Gross and Rotwein similarly generated a translocation-based Akt kinase sensor by fusing full-length FoxO1 to the green FP Clover,113 whereas Maryu et al. utilized the central region of FoxO3a, which contains the Akt phosphorylation site and phospho-regulated nuclear localization (NLS) and export (NES) sequences, to generate their own Akt kinase sensor.114 Regot and colleagues have also demonstrated the generalizability of this approach by engineering a family of optimized kinase translocation reporters (KTRs) based on a minimal translocation domain that contains a kinase-specific substrate peptide fused in tandem to a bipartite NLS115 and an NES, such that phosphorylation inhibits the NLS and activates the NES116 (Figure 1B). This design was successfully applied to develop KTRs for monitoring JNK, p38, extracellular signal-regulated kinase (ERK), and PKA activity.116 A similar technology known as synthetic kinase activity relocation sensors (SKARS) was also reported by Durandau et al. based on the phosphorylation-dependent charge disruption of a tandem NLS sandwiched between a MAPK substrate domain and an FP.117 2.3.2. Directly Sensitizing the Chromophore of a Fluorescent Protein. GFP represents an ideal fluorescent tag because it requires no exogenous cofactors and instead forms its chromophore intrinsically through the autocatalytic cyclization of three consecutive amino acids (e.g., S65, Y66, and G67 in native Aequorea victoria GFP) encoded in its primary sequence (see ref 11 for a more detailed overview of GFP chromophore formation). The native chromophore further adopts either of two chemical states, a major “neutral” species in which the phenolic −OH of Y66 is protonated and a minor “anionic” species in which the Y66 phenolic −OH is deprotonated, which are responsible for the dual-excitation, single-emission behavior of wild-type GFP (wtGFP) via conversion of the neutral to the anionic chromophore through 11722


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Figure 2. Single-FP fluorescent biosensor designs for cellular analytes and membrane potential. (A) Insertion of a sensing unit into an FP. Calmodulin, mEpac, and mPDE5α undergo conformational changes in response to binding Ca2+,152 cAMP,171 and cGMP,172 respectively, which perturbs the chromophore and alters the fluorescence. Binding can either lead to an increase in fluorescence, as seen in the Ca2+ biosensor camgaroo1,152 or a decrease in fluorescence, as seen in the cAMP biosensor flamindo.171 (B) Sandwiching a cpFP between sensing units. Biosensors have utilized sensing units that comprise either separate receiver and switch domains156,158 or split proteins that reconstitute during protein folding.173−175 For example, GCaMP biosensors utilize separate domains of CaM and M13, where calcium binding to CaM promotes the binding of CaM to M13 and results in a conformational change that leads to an increase in GFP fluorescence. On the other hand, the membrane voltage sensor ASAP1 inserts cpGFP into the voltage-sensing domain of the chicken voltage-sensitive phosphatase Gg-VSP, which reconstitutes after folding, and depolarization leads to a conformational change in the 4th transmembrane segment that alters the fluorescence of cpGFP.175 (C) Insertion of an FP into a voltage-sensitive channel. The conformational changes induced in voltage-sensitive K+ and Na+ channels alter the fluorescence of GFP to act as biosensors of membrane potential.176,177

excitation peaks responded oppositely to pH, with the ratio of intensity at each excitation wavelength ultimately serving as the readout (e.g., “ratiometric” pHluorin).121 Given that fluorescence intensity in cells is often influenced by multiple factors that are unrelated to the parameter being detected (e.g., variable expression, cell shape/thickness, sample illumination, bleaching, etc.), such ratiometric sensors are often advantageous for live-cell imaging because collecting fluorescence at two wavelengths that show opposite changes in response to a cellular parameter largely cancels out these variations, while also providing more quantitative measurements.134,135 Thus,

YFP(H148Q) via random mutagenesis, resulting in variants with enhanced affinity for either Cl− (H148Q/V163S; Kd ∼ 40 mM) or I− (H148Q/I152L; Kd ∼ 2 mM).132 However, the fact that YFP(H148Q) is sensitive to both pH and Cl− can complicate its application as a Cl− sensor. Thus, Zhong et al. also recently performed a series of mutagenic screens to generate monomeric Cl-YFP (EYFP-F46L/Q69K/H148Q/ I152L/V163S/S175G/S205 V/A206 K), which binds Cl− with a Kd of 14 mM and has a pKa of only 5.9.133 As part of their original screen for pH-sensitive GFP variants, Miesenböck et al. also identified a mutant whose dual 11723


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conformation of the fused protein such that conformational changes are transmitted into the FP β-can to alter chromophore behavior. Given that the native N- and Ctermini of FPs are unstructured, conformational coupling can also be enhanced through the use of circular permutation, which has long been used to study the structure and function of biological macromolecules151 and refers to the rearrangement of the linear sequence of a macromolecule in order to shift the positions of the native termini along the molecule surface without altering the overall 3D structure. Several FPs have been shown to tolerate circular permutation while retaining fluorescence behavior,152 with the resulting cpFPs possessing new termini located within the β-can itself; the added rigidity thus improves the transmission of conformational changes into the β-can. As such, inserting an FP into a conformational switch, or vice versa, provides a mechanism to sensitize the photophysical properties of a single FP to a given biochemical parameter (Figure 2). 2.3.3.1.1. Genetically Encoded Calcium Indicators. Advances in live-cell imaging are intimately linked with the study of Ca2+ signaling, which can be attributed to the long history of efforts to visualize Ca2+ dynamics as a proxy for neuronal activity. Indeed, the use of fluorescent indicator dyes was largely popularized by the success of Ca2+ indicators as live-cell probes (also discussed in section 4.2.1 below), while the discovery of GFP was itself an offshoot of work that yielded another live-cell Ca2+ probe, the photoprotein aequorin.153 Thus, genetically encoded Ca2+ indicators (GECIs) were naturally among the earliest biosensors to be engineered and remain a major focus of biosensor development efforts to this day. Barring the notable exception of CatchER,148 which is based on an engineered Ca2+ binding site, the development of singleFP GECIs has universally centered around calmodulin, a ubiquitous intracellular Ca2+ sensor and major effector of Ca2+ signaling in all eukaryotes (reviewed in refs 154 and 155), which undergoes a switchlike conformational change upon Ca2+ binding and recognition of its target proteins. In their work developing the first cpFPs, for example, Baird and colleagues found that inserting Xenopus laevis calmodulin into EYFP yielded a chimeric protein that displayed an approximately 7-fold increase in fluorescence intensity in the presence of Ca2+ in vitro and functioned as a Ca2+ indicator, nicknamed “camgaroo1”, in living cells152 (Figure 2A). Nagai and coworkers modified this design by utilizing a bipartite molecular switch.156 In this scheme, calmodulin was fused to the Cterminus of cpEYFP, while the M13 peptide, which is derived from the calmodulin-binding region of myosin light-chain kinase,157 was fused to the N-terminus. Calmodulin forms a compact complex with M13 upon Ca2+ binding, and the M13 peptide thus acts as a switching domain to promote a Ca2+dependent conformational change in the sensor (Figure 2B). These efforts ultimately yielded a family of GECIs known as “pericams”156 (Table 1). In a parallel effort, Nakai and co-workers set out to construct a GECI by first testing multiple fusion sites for linking the M13 peptide and calmodulin to the N- and C-termini of cpEGFP in a design scheme similar to that of pericams.158 Notably, the two early candidates with the best responses both introduced a 5-residue gap in the β-can structure where the molecular switch is inserted. Subsequent optimization focused on the linker sequences at interdomain junctions, and the 85th variant tested, which showed the highest basal fluorescence and Ca2+-

Hanson and colleagues developed a family of emissionratiometric pH sensors called “dual-emission GFPs” (deGFPs), which switch from green to blue emission with decreasing pH,134 while Bizzarri and co-workers also found that GFP F64L/S65T/T203Y/L231H, or E2GFP,136,137 can serve as both an excitation- and an emission-ratiometric pH sensor.138 To generate a ratiometric Cl− sensor, Kuner and Augustine utilized FRET between YFP and a tethered CFP, which is not sensitive to Cl− ions.139 In the resulting sensor, Clomeleon, Cl− binding decreases YFP fluorescence, thereby reducing FRET and dequenching CFP emission.139 Markova et al. used a similar approach to generate the ratiometric “Cl-sensor” but instead utilized a YFP mutant with increased Cl− sensitivity.140 Interestingly, E2GFP, which contains the T203Y mutation found in YFPs, is also sensitive to halide ions but does not display an intrinsic ratiometric response.141 However, because pH and Cl− differentially affect the E2GFP spectrum, Arosio and colleagues were able to generate a ratiometric sensor capable of monitoring both parameters (ClopHensor) by linking E2GFP to the monomeric RFP DsRedm.142 2.3.3. Engineered Modulation of the Photophysical Behavior of a Fluorescent Protein. The above examples represent a somewhat unique case in which the FP doubly performs as both the sensing and reporting unit. However, this design scheme is not very generalizable beyond the detection of a few select ions, as most cellular analytes and biochemical reactions do not directly affect the chromophore. Instead, a more universal strategy involves returning to the use of an extrinsic sensing unit that is engineered to couple the detection of a biochemical signal to the modulation of fluorescence behavior. As exemplified by the development of certain redox143,144 and metal ion sensors145−147 such as the ER Ca2+ sensor CatchER,148−150 the sensing unit can be as simple as a few amino acids inserted into the surface of an FP. In broader practice, however, a more sophisticated design is often required in which the sensing unit comprises a conformationally dynamic element capable of adopting either of two states (i.e., conformations) and readily switching between them in response to a specific input signal. Sensing units containing such “molecular switches” can often be generated from intrinsic conformational switches found in many native proteins or artificially engineered by linking isolated proteins or peptides in a two-component sensing unit that contains a “receiver” domain to sense the signal of interest and a “switch” domain to trigger the conformational change. Below, we describe how molecular switches can be utilized to modulate the photophysical behavior of one or more coupled FPs in order to directly visualize a vast array of biochemical and cellular parameters in living cells. 2.3.3.1. Designs Based on the Modulation of a Single Fluorescent Protein. The stereotypical β-can architecture of an FP not only protects the chromophore from the external environment but also is directly responsible for providing the internal microenvironment that supports fluorescence. Any alteration that distorts this β-can conformation can thus affect chromophore behavior, allowing conformational changes in a coupled molecular switch to directly influence FP fluorescence. One way to achieve conformational coupling is to directly insert an FP into a target protein. Because the native N- and Ctermini of an FP are located in close proximity in the folded 3D structure, FP insertion typically leads to minimal perturbation of the native conformation of the target protein, while at the same time rendering the FP sensitive to the 11724


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dependent response, was named “GCaMP”.158 Tallini et al. were able to improve GCaMP behavior by introducing several mutations previously shown to improve GFP stability (V163A, S175G)159 or promote monomer formation (A206 K),160 while also identifying new substitutions (D180Y, V93I) via random mutagenesis.161 Adding an N-terminal RSET leader sequence also helped improve thermal stability, resulting in GCaMP2. A mammalian-cell screen for point mutants with increased brightness, dynamic range, and Ca2+ sensitivity then identified two mutations in GFP (M153 K, T203 V) and a third in calmodulin (N60D), resulting in a third-generation sensor (GCaMP3) with 3-fold higher basal fluorescence and a 2-fold higher dynamic range. GCaMP3 could thus be used to successfully image Ca2+ dynamics in brain slices and in vivo.162 Given the importance of GECIs for imaging neuronal activity, one area of focus for continued engineering efforts has been to enhance the speed and sensitivity of GCaMP responses in neurons to more accurately report the dynamics of action potentials. For instance, Akerboom and colleagues generated a panel of 12 GCaMP5 variants, 3 of which (GCaMP5A, 5G, and 5K) exhibited 2- to 3-fold better SNR and more faithfully recapitulated electrophysiological recordings of in vivo neuronal activity compared with GCaMP3.163 Chen et al. extended this work even further by screening for optimized GCaMP5G variants directly in cultured neurons to obtain GCaMP6s, 6m, and 6f with slow, medium, and fast kinetics, respectively.164 Of equal importance has been the development of a wider array of GECI color variants, especially red-shifted sensors, which are particularly desirable for in vivo imaging given the reduced phototoxicity, decreased absorption and scattering, and thus deeper tissue penetration, associated with longer-wavelength illumination. Zhao and colleagues, for example, developed a red-shifted GECI by replacing the cpEGFP domain of GCaMP3 with a circularly permuted version of the RFP mApple,165 which after several rounds of directed evolution yielded “R-GECO1”.166 However, mApple is known to exhibit substantial photoswitching behavior,165 and subsequent studies found that this behavior is preserved in R-GECO1 and its descendants,167,168 which significantly increase in intensity upon illumination with blue light, thus potentially limiting their compatibility with optogenetic tools. As part of an independent effort, Akerboom and co-workers168 elected to replace the cpEGFP domain of GCaMP3 with mRuby.169 A dimly fluorescent “RCaMP” prototype was subjected to multiple rounds of both random and structure-guided mutagenesis to produce a series of improved variants culminating in RCaMP1h. Importantly, despite possessing a lower dynamic range, RCaMP1 did not exhibit any of the complex photophysical effects that characterize mApple-based GECIs. RCaMP1 was successfully employed for dual-color Ca2+ imaging along with GCaMP5G in mixed neuronal/astrocyte cultures, for in vivo imaging, and for combined optogenetics/imaging studies.168 Recently, Dana and colleagues were able to further optimize these red GECIs by screening for variants directly in cultured neurons, yielding three improved probes, mRuby-based jRCaMP1a and jRCaMP1b and mApple-based jRGECO1a.170 Yet despite their promise as powerful tools for interrogating neuronal function in vivo, red GECIs nevertheless continue to lag behind GCaMPs in overall dynamic range, and evidence also suggests that these probes produce multiple species in cells due to incomplete chromophore maturation.170 Thus, additional

optimization remains a priority for these sensors, likely entailing re-engineering of the RFP itself. 2.3.3.1.2. Genetically Encoded Voltage Indicators. Plasma membrane voltage is a key indicator of electrical activity in neurons. Thus, in addition to GECIs for Ca2+ imaging, the potential to use genetically encodable probes to directly visualize changes in membrane voltage in specific neuronal subsets has also spurred the development of genetically encoded voltage indicators (GEVIs). GEVIs couple changes in membrane potential to changes in fluorescence by taking advantage of the fact that multiple cellular proteins contain a voltage-sensing domain (VSD) that enables them to sense and respond to changes in membrane potential. VSDs consist of a group of four helical segments (S1−S4) that sit in the plasma membrane, with the positively charged S4 undergoing a structural rearrangement in response to voltage changes (otherwise known as “gating”). These conformational changes can be transmitted into the barrel of a coupled FP, thereby allowing a VSD-containing protein to serve as a voltagedependent molecular switch to control biosensor fluorescence. Initial efforts relied on voltage-gated ion channels as the source of the VSD. For example, Siegel and Isacoff constructed the very first GEVI by inserting a C-terminally truncated form of GFP into the plasma membrane-proximal region of the cytosolic tail of the Shaker potassium channel, just after the sixth transmembrane segment of the channel176 (Figure 2C). Similar to the reasoning underlying the use of cpFPs in singleFP-based sensors, the unstructured C-terminus of GFP was deleted based on the hypothesis that direct fusion of Shaker to the less flexible β-can would improve conformational coupling and voltage-dependent fluorescence changes. A W434F mutant form of Shaker, in which the ion-conducting pore is disrupted without affecting channel gating, was also used to prevent overexpression of the sensor from altering cell physiology. However, although the resulting “fluorescent Shaker” (FlaSh) protein displayed a clear decrease in GFP fluorescence upon membrane depolarization in Xenopus oocytes, the kinetics of the fluorescence response were ∼30-fold slower than the actual gating kinetics of the channel and thus ill-suited to imaging the rapid dynamics of neuronal action potentials.176 Ataka and Pieribone similarly constructed a GEVI by using the μI voltage-gated sodium channel as the VSD.177 In contrast to potassium channels, sodium channels function as monomers, which offers a wider selection of FP insertion sites and also eliminates the risk that the sensor will reconstitute with native channel subunits. After screening 10 different insertion sites, the authors found that inserting full-length EGFP between the second and third transmembrane regions yielded a sodium channel protein-based activity reporting construct (SPARC) (Figure 2C). Importantly, although SPARC exhibited a much smaller fluorescence change compared with FlaSh, the response kinetics were fast enough to report voltage pulses on the order of 2 ms.177 Although successful as a proof of concept, GEVIs based on ion channels can be difficult to express in cells and exhibit poor membrane localization. Thus, subsequent efforts have simplified GEVI architecture by turning toward more compact VSDs, beginning with the discovery of the Cionia intestinalis voltage-sensitive phosphatase (CiVSP).178 Genomic surveys found that this sea squirt (family Ascidiascea) expresses a lipid phosphatase fused to a single VSD, which is homologous to those found in voltage-gated potassium channels yet functions as a monomer. As with the VSDs from voltage-gated channels, 11725


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For instance, exchange protein regulated by cAMP (Epac), a Rap1/2 guanine nucleotide exchange factor (GEF) regulated by the ubiquitous intracellular messenger cAMP, has been shown to undergo a major conformational change in response to cAMP binding.187,188 Kitaguchi and colleagues were thus able to develop a yellow-fluorescent cAMP sensor by inserting the murine homologue of Epac1 (mEpac1) into Citrine.171 A screen of 12 variants featuring different mEpac1 fragments and linkers yielded a successful “fluorescent cAMP indicator” (Flamindo) containing just the mEpac1 cyclic nucleotide binding domain (CNBD) and exhibiting an ∼50% cAMPdependent intensity decrease (Figure 2A). Odaka et al. subsequently improved the response by mutating the Nterminal Citrine-mEpac1 junction, with the resulting Flamindo2 sensor exhibiting an ∼75% intensity decrease in response to cAMP.189 Tewson and co-workers also constructed “cAMP difference detector in situ” (cADDis), which shows a ∼35% cAMP-dependent fluorescence decrease, by inserting a circularly permuted version of the bright green FP mNeonGreen190 into the hinge region of Epac2.191 Finally, Harada et al. recently generated the red-shifted “Pink Flamindo” cAMP sensor by inserting the mEpac1 switch from Flamindo into mApple and screening multiple linker variants.192 Pink Flamindo, which exhibits a 4-fold cAMP-dependent increase in fluorescence, was successfully coimaged with a greenfluorescent GECI (G-GECO166), yet it remains to be seen whether future applications of this sensor face the same photoswitching behavior as other mApple-based probes. Likewise, 3′,5′-cyclic guanosine monophosphate (cGMP) is another key intracellular messenger that regulates numerous cellular processes, especially in relation to vascular and neuronal biology.193 As with cAMP, cGMP exerts its biological effects through the regulation of a number of protein targets, such as the cGMP-dependent protein kinase (PKG), which has been shown to undergo isoform-specific conformational changes in response to cGMP binding.194,195 In fact, Nausch and co-workers previously found that inserting cpEGFP into the C-terminal tail region of the tandem CNBDs isolated from PKG I was sufficient to yield a cGMP biosensor.173 By incorporating the CNBD regions from the α, β, or δ isoforms of PKG I, they were thus able to generate a family of “fluorescent indicators of cGMP”, or FlincGs, that each displayed a range of cGMP-binding affinities while all showing fairly robust cGMP-dependent fluorescence increases in cells. More recently, Matsuda et al. developed a cGMP sensor using another cGMP-binding domain known as a GAF domain,172 which is present as an allosteric regulatory domain in many enzymes, including certain phosphodiesterase (PDE) isoforms, and is also known to undergo a conformational change upon cGMP binding.196,197 Similar to the design of Flamindo, the authors inserted the GAF-A domain from murine PDE5α (mPDE5α) into citrine and screened several mPDE5α fragments and linker sequences to obtain the single-FP biosensor “Green cGull”, which exhibited an ∼8-fold cGMPdependent fluorescence intensity increase, as well as 3 orders of magnitude higher affinity for cGMP (∼1 μM) compared with cAMP (∼1 mM).172 A number of studies have also explored the potential for bacterial periplasmic binding proteins (PBPs) to serve as scaffolds for biosensor engineering. PBPs are responsible for mediating the uptake of various nutrients and other solutes in Gram-positive bacteria such as Escherichia coli and are therefore capable of binding a wide range of small

the fourth helical segment in the VSD of CiVSP (i.e., Ci-VSD) undergoes a voltage-dependent structural rearrangement within the plasma membrane, which can be directly coupled to an FP, though this requires incorporating point mutations in the VSD to shift voltage sensitivity into the physiological range of membrane potential in neurons.179 Notably, electrophysiological studies performed by Lundby and co-workers found that the Ci-VSD produces a very rapid gating current (i.e., current generated by the movement of positively charged segment 4 within the membrane) in response to membrane depolarization.180 Thus, by fusing the CFP variant Cerulean181 directly to the C-terminus of Ci-VSD using a short linker, they were able to generate an improved voltage-sensitive fluorescent protein (VSFP) with a robust voltage-dependent fluorescence change and ∼1 ms activation kinetics. Taking their inspiration from the development of GECIs, Barnett and colleagues similarly appended cpEGFP to the C-terminus of Ci-VSD.182 Screening through 90 constructs that varied either the fusion site between Ci-VSD and cpEGFP or the precise residues surrounding the circular permutation site yielded the sensor “ElectricPk”, which also exhibited a rapid fluorescence response capable of resolving 1 ms voltage pulses.182 More recently, St-Pierre et al. based the design of their single-FP GEVI, “accelerated sensor of action potentials” (ASAP1), on structural studies of the gating mechanism of VSDs.175 Specifically, given reports demonstrating a large conformational change within the loop connecting helical segments S3 and S4,183 they inserted a circularly permuted superfolder GFP (cpsfGFP)37 into this loop region in the chicken (Gallus gallus) VSD (Gg-VSD) (Figure 2B). The loop connecting S3 and S4 is shorter in Gg-VSD than in Ci-VSD, which the authors reasoned would increase conformational coupling to cpsfGFP. ASAP1 exhibited fast (∼2 ms) kinetics, as well as a much higher dynamic range compared with previous GEVIs.175 To develop an optimized GEVI that was suitable for use with 2-photon imaging of in vivo neuronal activity in the Drosophila visual system, Yang and colleagues generated variants of ASAP1 by mutating the junction sequence connecting S3 from Gg-VSD to cpsfGFP.184 These efforts yielded the improved variant ASAP2f, which produced a larger voltage-dependent fluorescence change in neurons compared with ASAP1 while exhibiting the same fast response kinetics. Abdelfattah et al. also recently reported the development of a red-shifted GEVI with comparable performance characteristics to optimized GFP-based GEVIs.185 Similar to the development of R-GECO1, the authors fused cpmApple to the S4 helix at the C-terminus of Ci-VSD and then performed directed evolution. After screening thousands of variants, they obtained “Fluorescent indicator for voltage imaging Red”, or FlicR1, which exhibited submillisecond activation and deactivation kinetics and a large dynamic range.185 Overall, the development of GEVIs is still an ongoing effort, with even greater improvements in sensitivity and dynamic range hopefully to come in the near future. 2.3.3.1.3. Single Fluorescent Protein-Based Indicators for Other Cellular Analytes. The “killer app” of the molecularswitch-as-sensing-unit design is versatility: given the availability of an endogenous effector protein that undergoes a suitable conformational change, this approach can be used to engineer biosensors for monitoring virtually any cellular analyte, or in some cases, ratios of analytes (e.g., ATP/ADP and NADH/ NAD+, discussed in ref 186), in living cells. 11726


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molecules.198,199 Members of this large protein superfamily generally share a similar protein architecture comprising a pair of globular domains that are linked by a hinge region, with ligand binding inducing substantial conformational rearrangement of the globular domains.200,201 In one early proof-ofconcept study, Marvin and colleagues utilized structural data on the E. coli maltose-binding protein (MBP) to identify candidate sites for the optimal insertion of cpGFP.202 Four different insertion sites were tested, and after screening libraries of linker variants, a candidate construct named MBP165-cpGFP.PPYF, or “PPYF”, was obtained that displayed a ∼2.5-fold increase in green fluorescence upon maltose binding. Similarly, Alicea et al. were able to generate a genetically encoded phosphonate sensor by inserting cpGFP into the E. coli EcPhnD protein.203 Most recently, Marvin and co-workers developed “iGluSnFR”, a single-FP sensor for monitoring the neurotransmitter glutamate that exhibits an up to 4-fold glutamate-dependent fluorescence intensity increase in cells.174 iGluSnFR was constructed by inserting cpGFP into the E. coli PBP Glt1 (Figure 2B); in keeping with the engineering of the above maltose and phosphonate sensors, a position close to the hinge region of Glt1 was chosen as the insertion point, followed by screening to optimize the interdomain junction sequences. PBPs thus show considerable promise as biosensor scaffolds and, given the relative ease with which their ligand-binding sites can be computationally redesigned,199 will potentially yield a myriad of new singleFP biosensors. GPCRs are another promising scaffold for biosensor engineering, as illustrated by the recent development of novel single-FP biosensors for monitoring endogenous neurotransmitters such as dopamine and acetylcholine. For instance, Patriarchi and colleagues inserted the cpEGFP module from GCaMP6 into the third intracellular loop (ICL3) of the dopamine D1 receptor (D1R) and screened 585 linker variants to obtain the dLight1.1 sensor.204 Further engineering, including the use of the D2R and D4R, yielded a family of 5 dopamine sensors (dLight1.1−1.5) with low nanomolar to micromolar affinity. Using a similar principle, Sun et al. systematically tested various insertion site and linker sequences to introduce cpEGFP into the D2R and then performed mutational screening to obtain a pair of dopamine biosensors, GRABDA1m and GRABDA1h, with moderate (∼130 nM) and high (∼10 nM) binding affinity, respectively.205 Jing and coworkers also succeeded in engineering a single-FP acetylcholine sensor, GACh1.0, by inserting cpEGFP into ICL3 of the M3 muscarinic acetylcholine receptor, after which they obtained an improved GACh2.0 sensor via linker optimization, including 723 single and 23 combinatorial mutants.206 Given the general applicability of these designs and the abundance of GPCRs, we can expect the development of single-FP biosensors for a variety of naturally occurring cellular analytes. 2.3.3.1.4. Engineering Single-Fluorescent-Protein Biosensors with a Ratiometric Readout. The single-wavelength, intensiometric biosensors discussed above are popular for in vivo imaging due to their large dynamic ranges and high SNR, as well as for biosensor multiplexing and all-optical (e.g., optogenetic) studies enabled by their small spectral footprint. Nevertheless, as alluded to previously, dual-wavelength ratiometric sensors are often preferable for canceling out artifacts related to probe expression and for obtaining quantitative measurements.

In some cases, ratiometric behavior can be obtained as an intrinsic property, in which the molecular switch induces a shift in the excitation or emission spectrum of a single FP. For instance, Nagai et al. were able to generate a ratiometric Ca2+ sensor by introducing an H203F mutation, which increases fluorescence by the protonated YFP chromophore,207 into the intensiometric “flash-pericam”.156 Screening identified two additional mutations (F46L, H148D) that, along with linker optimization, improved the response to yield a “ratiometricpericam” that undergoes a Ca2+-dependent shift from ∼415 nm to ∼495 nm excitation. More recently, a trio of ratiometric GECO-family Ca2+ indictors were also identified through screening that undergo a Ca2+-induced shift from blue (∼450 nm) to green (∼510 nm) emission upon ∼400 nm excitation (“GEM-GECO”), from ∼400 nm to ∼488 nm excitation with ∼510 nm (green) emission (“GEX-GECO”), and from ∼575 nm to ∼480 nm excitation with ∼600 nm (red) emission (“REX-GECO”).166,208 Zhao and colleagues also obtained a ratiometric sensor for monitoring the intracellular NADH/ NAD+ concentration ratio after inserting cpYFP into the interdomain loop of the Thermus aquaticus Rex (T-Rex) protein.209 This sensor, named “SoNar”, undergoes an increase in the 420/480 nm excitation ratio upon NADH binding, whereas NAD+ induces the opposite response. By computationally redesigning the T-Rex binding pocket in SoNar, Tao et al. were then able to generate a ratiometric sensor that directly reports NADPH concentrations, yielding a family of “iNap” sensors with a range of affinities.210 Finally, we recently developed a novel suite of excitation ratiometric kinase activity reporters (ExRai-KARs) in which cpGFP undergoes a phosphorylation-dependent shift from ∼400 nm to ∼488 nm excitation with ∼515 nm (green) emission.211 Alternatively, a more straightforward approach for generating ratiometric sensors is simply to add a second, spectrally distinct FP as an internal reference. Hung and colleagues, for example, also generated a ratiometric NADH/NAD+ redox sensor, “Peredox”, by inserting the long-Stokes-shift FP cpTsapphire212 between a tandem dimer of T-Rex proteins and fusing mCherry213 to the C-terminus.214 cpT-Sapphire exhibits an increase in fluorescence intensity upon NADH binding to T-Rex, whereas mCherry fluorescence remains unaltered, thereby providing a ratiometric readout. Recently, Cho et al. used a similar approach to design ratiometric versions of GCaMP by fusing mCherry to the C-terminus of GCaMPs 3, 6s, and 6f.215 However, they included a rigid ER/K α-helical linker216,217 between calmodulin and mCherry, reasoning that this 30 nm spacer would eliminate potential FRET with cpGFP. The resulting GCaMP-R3, GCaMP-R-6s, and GCaMP-R-6f sensors all displayed large emission ratio changes and enabled quantitative Ca2+ imaging in cells and tissues. Meanwhile, Ast and co-workers added a twist to this approach by creating a nested FP pair, in which a long-Stokes-shift OFP (LSSmOrange218) is inserted into the cpGFP linker, to develop “GO-Matryoshka” (so named after the famous Russian dolls).219 Importantly, the use of LSSmOrange allows both FPs to be excited at a single wavelength (∼440 nm), while the lack of spectral overlap between cpGFP emission and LSSmOrange absorption prevents FRET. This approach successfully yielded the ratiometric Ca2+ sensor MatryoshCaMP6s (derived from GCaMP6s), as well as the ammonium sensor AmTryoshka1;3, which was constructed by inserting GO-Matryoshka into the Arabidopsis thaliana ammonium transporter AtAMT1;3.219 11727


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Figure 3. Designs of FRET-based reporters for ions, cellular analytes, and membrane potential. (A) FRET-based metal ion biosensors utilize receiver and switch domains that bind to each other376,421 or endogenous proteins that undergo a conformational change410 in response to binding of metal ions. These conformational changes alter the intramolecular distance and orientation of a pair of FPs and thus changes the amount of energy transferred from the donor FP to the acceptor FP. (B) Voltage sensors that utilize a FRET-based readout often rely primarily on changing the orientation of FP dipoles.258,271 VSFP1 contains a CFP inserted between the 3rd and 4th transmembrane domain and a YFP fused to the C terminal tail of a truncated portion of the rat Kv2.1 channel where cell depolarization induces a conformational shift in the 4th transmembrane domain, thus changing the relative angle of the two dipoles of the FPs.271 (C) Several biosensors for cellular analytes have utilized the design of sandwiching a conformationally switching domain between a FP FRET pair.299,311,314,325,327,349,362,540,556 For example, the cAMP biosensor ICUE2 utilizes the conformational change induced by cAMP binding to the CNB domain of Epac1 to increase the intramolecular distance between the donor and acceptor FPs.311 Alternatively, having both a PIP3 binding PH domain from GRP1 and a C-terminal membrane localization sequence in combination with engineered rigid α-helical linkers yielded a chimeric protein that exhibits significant conformational changes in response to PIP3 production, leading to changes in FRET between a FRET pair flanking the chimeric protein.556

2.3.3.2. Designs Based on the Modulation of a Pair of Fluorescent Proteins. Far from providing a mere reference signal, a second FP can be directly coupled to the sensing unit such that both FPs function as the reporting unit, with the molecular switch inserted between a pair of FPs to modulate the photophysical interaction between them. Compared with the engineered modulation of a single FP by a molecular switch, this dual-FP sandwich configuration represents a more straightforward and accessible design scheme that is less strictly reliant on extensive protein engineering and, to date, remains the most popular and generalizable approach to biosensor design. The most common implementation of this dual-FP design scheme involves sandwiching a molecular switch between two FPs that constitute a FRET pair. Recall

from section 2.2.2 that FRET efficiency not only falls off with the sixth power of the interfluorophore distance but also requires the donor and acceptor dipoles to be properly oriented relative to one another. In addition to monitoring interactions between proteins (i.e., intermolecular FRET), this proximity and orientation dependence therefore make FRET an excellent reporter of conformational changes within a protein (i.e., intramolecular FRET), allowing the straightforward coupling of a biochemical signal to a change in FRET and forming the basis for a seemingly endless array of genetically encoded FRET-based biosensors. 2.3.3.2.1. FRET Sensors for Monitoring Cellular Analytes. Much like engineered single-FP biosensors, FRET-based sensors are often built by incorporating a sensing unit that 11728


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undergoes an engineered or intrinsic conformational change in response to the binding of an intracellular messenger or other small molecule. As detailed below, this approach has yielded numerous FRET-based sensors engineered to monitor the dynamics of metal ions, cyclic nucleotides, phospholipids, and various other analytes within living cells. 2.3.3.2.1.1. FRET-Based Indicators of Metal Ion Concentrations. The very first GECIs were also among the first ever genetically encoded FRET-based biosensors. Specifically, Miyawaki and colleagues generated the sensor “cameleon” by sandwiching a Ca2+-sensitive molecular switch, composed of calmodulin linked to the M13 peptide via a diglycine linker, between the FRET donor BFP and the FRET acceptor GFP.377 In a design that would later inspire additional classes of FRET-based biosensors (see below), the reversible binding of Ca2+ causes the sensing unit to switch between an extended conformation and a more compact form in which calmodulin essentially engulfs the M13 peptide, thereby altering the relative proximity, and thus the efficiency of FRET, between BFP and GFP. As discussed above, changes in FRET cause inverse changes in the intensity of the acceptor and donor fluorophores, thereby altering the ratio of fluorescence emission and providing a dynamic, quantitative readout of intracellular Ca2+ elevations. However, although cameleon performed well as a Ca2+ indicator in vitro, the low brightness of the BFP donor fluorophore rendered cameleon less effective when expressed in mammalian cells. Substitution of CFP and YFP in place of the BFP/GFP FRET pair was therefore performed to yield “yellow-cameleon”, or “YC” (Figure 3A), which exhibited higher brightness in mammalian cells and was also less affected by cellular autofluorescence.377 One of the primary motivations for developing cameleons, as well as all subsequent GECIs, was to enable the direct visualization of highly localized Ca2+ signaling events via subcellular targeting (discussed further in section 4.2.1). However, early efforts to investigate local Ca2+ elevations in neurons revealed that YC suffers a considerable reduction in Ca2+ sensitivity when targeted to the plasma membrane (discussed in refs 352 and 382). This effect was attributed to intramolecular interactions between the sensor and endogenous calmodulin, which is present at high concentrations in the vicinity of the plasma membrane due to its role as a binding partner for Ca2+ channels.665,666 Indeed, a key consideration in biosensor design is the potential for the sensing unit, which is derived from cellular proteins, to interact with endogenous components within cells, which can both perturb biological functions and also disrupt biosensor responses. Thus, to develop a sensor that was less likely to experience these effects, Heim and Griesbeck replaced the calmodulin/M13 molecular switch with one derived from troponin C (TnC), a musclespecific Ca2+ sensor that plays a far less central role in intracellular Ca2+ signaling, yielding the sensors TN-L15, based on chicken skeletal muscle TnC, and TN-humTnC, based on human cardiac TnC.410 Alternatively, Palmer and colleagues opted to retain the original YC design scheme and instead computationally re-engineered the complementary “bumps” and “holes” that make up the calmodulin-M13 binding interface, yielding a panel of designed, or “D”-series, YCs that are largely orthogonal to their endogenous intracellular counterparts, albeit with a notable reduction in affinity.371 Importantly, both approaches produced FRET-based GECIs capable of reporting local Ca2+ dynamics at the plasma membrane.371,410

GECIs are highly desirable tools for monitoring neuronal activity, and FRET-based GECIs are no exception. Thus, considerable efforts have been devoted to enhancing the speed and sensitivity of FRET-based GECIs to reliably measure physiological Ca2+ dynamics in living neurons. As alluded to in section 2.3.3.1.1, the Ca2+-binding affinity of GECIs can be tuned within a fairly broad range by mutating the two Ca2+binding “EF-hand” motifs located in both of the globular domains of calmodulin,155 and this approach has successfully yielded YC variants with Ca2+-binding affinities spanning several orders of magnitude.377 In addition, the original calmodulin-M13 switch design used in these probes is based on in vitro studies showing that a hybrid protein in which calmodulin and M13 are fused by a diglycine linker undergoes a Ca2+-dependent switch from an extended “dumbbell” shape to a more compact, globular form.667 Because the binding of Ca2+-bound calmodulin (Ca2+/CaM) to target peptides is known to slow the dissociation of Ca2+ from calmodulin, thereby increasing apparent Ca2+-binding affinity,668 shifting the conformational equilibrium toward the compact form is expected to increase the Ca2+-binding affinity of the hybrid complex, and indeed, the incorporation of a 5-residue linker was found to increase the Kd compared with the original diglycine.669 Following this logic, Horikawa and colleagues were therefore able to generate YC variants with enhanced Ca2+-binding affinities by lengthening the calmodulin-M13 spacer from the original 2 to between 3 and 5 residues, yielding “YC-Nano” sensors with dissociation constants as low as 15 nM, which were sensitive enough to visualize spontaneous in vivo neuronal activity in zebrafish embryos.370 Meanwhile, parallel efforts have yielded improved TnCbased GECIs with very fast response kinetics for neuronal imaging. Like calmodulin, TnC is also a “dumbbell”-shaped molecule that contains a pair of EF-hand motifs in each of its globular “head” domains.670 Whereas the N-terminal regions specifically bind Ca2+, the C-terminal domains are only partially selective and are capable of binding Mg2+, which also results in more complex Ca2+-binding kinetics.671,672 However, by introducing mutations based on the Ca2+-binding sites of calmodulin into EF-hands III and IV within the Cterminal domain of TnC, Mank and colleagues were able to eliminate Mg2+ binding to the TN-L15 sensor and obtain an improved variant, TN-XL, that exhibited very fast Ca2+ binding kinetics, with an off-rate of ∼140 ms409. However, these mutations somewhat decreased the affinity of TN-XL for Ca2+ compared with TN-L15 (∼2 μM vs ∼750 nM), potentially reducing its effectiveness in measuring small neuronal Ca2+ transients. These authors were subsequently able to boost the sensitivity of this probe by replacing N-terminal EF-hands I and II with a duplicate copy of domains III and IV to generate TN-XXL (Kd ∼ 800 nM).408 More recently, Thestrup et al. set out to minimize the structure of TnC-based Ca2+ sensors by reducing the number of Ca2+-binding sites in the molecular switch, reasoning that fewer binding sites would yield a sensor with a simpler, more linear response behavior.405 Although initially unsuccessful in obtaining a high-affinity, single-EFhand derivative of the chicken TnC used in previous TN sensors, the authors were ultimately able to construct a highaffinity Ca2+ sensor by sandwiching a single functional EF-hand from the oyster toadfish Opsanus tau between CFP and YFP. The resulting “Twitch” sensors retained the rapid kinetics of the previous TN-XL/XXL sensors while exhibiting dissociation constants as low as 150 nM.405 11729


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terminus of the S4 helix.267 Progressively lengthening the spacer between S4 and CFP yielded four variants, VSFP2A-D, with longer linkers producing larger FRET changes. However, the voltage sensitivity of all four variants was well outside the range of physiological membrane potentials observed in mammalian cells, and an R217Q mutation was therefore incorporated into the VSD to produce a sensor (VSFP2.1) with a more physiological response range.267 This same design has been carried forward in subsequent FRET-based GEVIs, such as VSFP2.3,180 VSFP2.4,265 and “Mermaid”.266 However, more recent probe designs have reconfigured the coupling between the sensing and reporting units by sandwiching the VSD between a FRET pair (Figure 3B), including VSFPButterfly,257,262 “Mermaid2”,258 and the recent Nabi sensors.256 2.3.3.2.1.3. FRET-Based Sensors for Monitoring Cyclic Nucleotide Dynamics. Most FRET-based cAMP biosensors utilize the intrinsic, cAMP-induced conformational change within either Epac1 or Epac2 as the basis of their sensing unit. For example, Nikolaev and colleagues generated Epac2-camps (“Epac2-based cAMP sensor”) by fusing YFP and CFP to the N- and C-termini of a minimal fragment spanning the second CNBD (CNBD-B) of Epac2299. Epac1-camps was similarly constructed using the solitary cAMP-binding domain of Epac1, which corresponds to CNBD-B from Epac2. Parallel work by DiPilato and co-workers alternatively utilized full-length Epac1 sandwiched between CFP and YFP to generate the “indicator of cAMP using Epac”, or ICUE,312 though subsequent versions of ICUE have utilized an N-terminally truncated form of Epac1309,311 (Figure 3C). However, despite the prominence of Epac-based designs, various FRET-based cAMP sensors have also been generated using cAMP-binding domains from other proteins. In fact, the very first genetically encoded FRET-based cAMP sensor, PKA-GFP, was based on the cAMP-induced dissociation of EGFP- and EBFP-tagged PKA catalytic (C) and regulatory (R) subunits,323 a design that was based on a previous fluorescent analog2 of PKA, called FlCRhR, featuring fluorescein and rhodamine conjugated to purified PKA C and R.675 Surdo and colleagues also recently generated a FRETbased cAMP sensor using the CNBD-B domain of the type II PKA regulatory subunit (RII) by fusing CFP to the C-terminus and inserting YFP into a flexible loop, yielding a “cAMP universal tag for imaging experiments” (CUTie).301 Meanwhile, Mukherjee et al. were able to construct a FRET-based cAMP sensor with nanomolar sensitivity by sandwiching the CNBD from a bacterial cyclic-nucleotide-gated potassium channel676 between CFP and YFP.314 Several different binding domains have also been used as sensing units to construct FRET-based cGMP reporters. Initial designs utilized nearly full-length forms of PKG Iα, which undergoes a cGMP-dependent conformational change to relieve autoinhibition.194 For example, both the CGY (“cyanG kinase-yellow”)327 and cygnet (“cyclic GMP indicator using energy transfer”)329 sensors were constructed by sandwiching N-terminally truncated PKG Iα between CFP and YFP. Because PKG Iα functions as a dimer, these N-terminal truncations were used to remove the dimerization sequences and thus avoid intermolecular FRET between individual sensors. Notably, whereas CGY sensors retain the N-terminal autoinhibitory domain and thus preserve the native regulation of PKG Iα,327 cygnet features a larger N-terminal deletion that also removes this domain, necessitating the incorporation of a Thr516Ala substitution to prevent the sensor from being

In addition to Ca2+, other metal ions also play critical roles in cells, especially transition metal ions, which often function as essential cofactors in enzyme catalysis. Because these ions, like Ca2+, are often toxic, cells have evolved numerous pathways to tightly regulate their intracellular accumulation,673 and components of these pathways have proven useful in the development of biosensors to monitor the dynamics of transition metal ion homeostasis in living cells. For example, van Dongen and colleagues set out to generate a copper biosensor based on the Cu(I)-induced dimerization between Atox1, a copper chaperone, and WD4, the fourth copperbinding domain of the “Wilson’s disease” protein (aka ATP7B), a copper-transporting ATPase.422 However, although this strategy did indeed yield a Cu(I)-induced change in intermolecular FRET between CFP-Atox1 and W4-YFP, the response was disrupted by the presence of free thiol groups (e.g., DTT), which disrupted the Cu(I)-mediated bridging of Cys residues within these two metallothioneins. Instead, this effort serendipitously yielded the first FRET-based Zn2+ sensor, as these two domains also exhibited a Zn2+-induced FRET change that was insensitive to free thiols.422 van Dongen et al. subsequently refined this design by incorporating a flexible linker between the metal-binding domains, yielding the CFP-Atox1-Linker-WD4-YFP, or CALWY, sensor (Figure 3A).422 Using a strategy analogous to that described above for Cameleons, Vinkenborg and colleagues then systematically modified the length of this linker to engineer a suite of CALWY sensors with a range of Zn2+-binding affinities.420 Qiao and co-workers were also able to construct a Zn2+ biosensor using the Zn2+-finger (ZF) 1 and 2 domains of the yeast Zap1 transcription factor.429 Zn2+ binding causes these domains to adopt a canonical ZF fold structure and form an interdomain complex, and sandwiching this molecular switch between CFP and YFP generated the ZF1/2 sensor. This design was further optimized by Qin and co-workers to yield ZapCY1 and 2,428 while Dittmer et al. similarly utilized a single canonical Cys2His2 ZF domain from the Zif268 transcription factor, which also adopts a folded structure upon Zn2+ binding, to generate a more compact FRET-based Zn2+ biosensor.426 Carter and colleagues also recently compared the performance of several Zn2+ sensors, providing a roadmap for obtaining quantitative measurements with minimal perturbation,674 which is crucial given the much smaller labile pools of intracellular Zn2+ and other metal ions versus Ca2+. 2.3.3.2.1.2. FRET-Based Voltage Indicators. The earliest FRET-based GEVIs, like their single-FP counterparts, were developed by utilizing full-length voltage-gated ion channels to provide the molecular switch in the sensing unit. For example, Sakai and colleagues constructed the original, FRET-based VSFP using the Kv2.1 voltage-gated potassium channel.271 To convert the channel into a biosensor, the authors joined CFP and YFP via a single-amino-acid linker and fused the resulting “CYFP” reporting unit after the S4 helix of a C-terminally truncated channel mutant. In this design, the voltage-induced rotation of the S4 helix is expected to alter the relative orientation of CFP with respect to YFP, thereby yielding a change in FRET (Figure 3B), and indeed, the resulting construct exhibited a nearly linear relationship between CFPsensitized YFP emission (i.e., FRET) and membrane potential.271 Dimitrov et al. subsequently developed an improved FRET-based VSFP biosensor family by replacing the Kv2.1 sensing unit with Ci-VSD, which they coupled to a similar tandem CFP-YFP reporting unit fused to the C11730


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constitutively active.329 However, finding that these early designs exhibited slow response kinetics, Nikolaev et al. sought to improve the temporal resolution by engineering simplified molecular switches based on isolated cGMP-binding domains, including the CNBD-B domain of PKG I, the GAF-A domain of PDE2A, and the GAF-B domain of PDE5325. Of these, the “cGMP energy transfer sensor” based on PDE5 (cGES-DE5), showed the best performance, including a large, rapid FRET change capable of reporting cGMP pulses. In contrast, Russwurm and co-workers observed slow in vitro response kinetics when they constructed a FRET sensor incorporating both the GAF-A and B domains of PDE5 and instead engineered a rapidly responding cGMP indicator (cGi) based on CNBD-A and B of PKG.326 2.3.3.2.1.4. FRET-Based Sensors for Lipid Messengers. As described in section 2.3.1, lipid-based messengers were a major focus of early biosensor development, yielding translocationbased fluorescent biosensors for detecting multiple lipid species. However, despite their continued utility, these sensors possess certain limitations, for instance, in their ability to probe lipid production in different subcellular compartments, as their reliance on translocation as a reporting mechanism is incompatible with subcellular targeting. Meanwhile, the development of FRET-based biosensors has provided an alternative reporting strategy that can be combined with targeting to specific subcellular sites. Like translocation-based fluorescent probes, FRET-based lipid sensors utilize endogenously derived lipid-binding domains. The most commonly used design was originally reported by Sato and colleagues, who generated the FRETbased PI(3,4,5)P3 sensor Fllip (“fluorescent indicator for lipid second messenger”) using three rigid α-helical linkers677 to position the PH-domain of Grp1 (PHGrp1) between CFP and YFP and also anchor the probe to a target membrane.556 A key aspect of this design is the presence of a diglycine “hinge” within the linker connecting PHGrp1 and YFP; thus, in the absence of PI(3,4,5)P3, the rigid linkers keep CFP and YFP separated, whereas PHGrp1 binding to PI(3,4,5)P3 causes CFP to “flip” outward and into closer proximity to YFP (Figure 3C). Swapping out the lipid-binding domain has allowed this design to be adapted to monitor other lipid messengers, including PI(4,5)P2 (PHPLCδ),542 PI(4)P (PHFAPP1),542 phosphatidic acid (PHSOS1, Dock2 C-terminal domain),549 and DAG (PKCβ C1 domain).541,542 Alternatively, Ananthanarayanan et al. constructed a FRET-based PI(3,4,5)P3/ PI(3,4)P2 sensor by tethering PHAkt to a negatively charged pseudoligand sequence,540 based on evidence that PHAkt recognizes head groups via a basic binding pocket.678 Sandwiching this switch between CFP and YFP yielded InPAkt (“indicator for phosphoinositides based on Akt”), wherein binding of PHAkt to PI(3,4,5)P3/PI(3,4)P2 displaces the pseudoligand and induces a FRET change, similar to the design of cameleon. 2.3.3.2.1.5. FRET-Based Sensors for Tracking Other Cellular Analytes. Numerous FRET-based biosensors have also been developed to illuminate the spatiotemporal dynamics of several additional analytes that are critical for cellular function. As discussed previously for engineered single-FP sensors (see section 2.3.3.1.3), the designs of these probes often take advantage of the diverse molecular machinery present in bacterial cells, whose genomes encode a variety of proteins capable of binding intracellular and extracellular analytes with high affinity and specificity. For example, Zhao

and colleagues recently developed a FRET-based NADP+ sensor (NADPsor) by sandwiching E. coli ketopantoate reductase, which undergoes a large conformational change due to the binding of NADP+ between its N- and C-terminal domains,679,680 between CFP and YFP.355 San Martiń and coworkers also recently developed FRET-based sensors for lactate (“lactate optical nano indicator from CECs [Centro de ́ Estudios Cientificos]”; Laconic) and pyruvate (pyronic) by similarly sandwiching the E. coli transcription factors LldR and PdhR, respectively, between a FRET pair.347,360 Notably, bacterial PBPs have proven to be a particularly rich resource for the development of genetically encoded fluorescent biosensors, given their broad target repertoire and wellcharacterized conformational changes (see section 2.3.3.1.3). Both the FLIPE (“fluorescent indicator protein for glutamate”)534 and GluSnFR (“glutamate sensing fluorescent reporter”)535,681 sensors, for instance, are based on the E. coli ybeJ/Glt1 protein, which is predicted to switch between open and closed conformations in response to the binding of glutamate. Meanwhile, PBPs have also yielded several FRETbased sensors that have been used to monitor the concentrations of various sugars, including maltose,349 glucose,336 ribose,361 sucrose,362 and more recently, trehalose.363 Recently, semisynthetic fluorescent biosensors, where proteins containing self-labeling domains (e.g., Halo Tag, SNAP-Tag) can be covalently conjugated in situ with synthetic fluorophores (reviewed in ref 10), have become a new avenue for development for genetically targetable probes of analytes. This biosensor design has been utilized to create a family of SNIFIT biosensors (SNAP-tag-based indicators with a fluorescent intramolecular tether) which include probes for GABA,533 glutamate,537 acetylcholine,531 NADP+,351 and NAD+.351 2.3.3.2.2. FRET Sensors for Monitoring Enzyme Activation/Activity. Along with sensing the dynamics of second messengers, metabolites, and other analytes, conformational changes in protein-based molecular switches can also be used to report on the biochemical functions of proteins themselves. Indeed, the sensitivity of FRET to intramolecular conformational changes has enabled the construction of genetically encoded biosensors capable of monitoring not only the activation of proteins in response to specific signaling events but also the endogenous catalytic activity of many key signaling enzymes directly within living cells. Sensors belonging to the former category, much like early FP-based sensor designs (see section 2.2), typically utilize a full-length protein of interest to serve as a proxy for its own behavior (e.g., activation), whereas sensors in the latter category generally make use of a surrogate substrate that can be catalytically modified by an enzyme of interest as part of an engineered molecular switch. FRET-based biosensors have thus opened a window onto the biochemical behavior of proteins in their native context that extends far beyond the ability to simply track protein expression, localization, and interactions. 2.3.3.2.2.1. FRET-Based Enzyme Activation Sensors. Many proteins undergo conformational changes in response to various signaling events (e.g., messenger binding), and while this phenomenon has proven extremely useful as a proxy for monitoring the dynamics of upstream signaling processes (e.g., messenger production), these same conformational changes can also be used to directly probe the activation dynamics of signaling proteins themselves. Often, this approach involves the heterologous overexpression of the protein of interest fused 11731


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Figure 4. FRET-based biosensor designs for signaling proteins. (A) Biosensors for signaling enzyme activation consist of whole or truncated portions of the enzyme sandwiched between an FP FRET pair.458,459,478,540 For example, the conformational change in the phosphatase CaNAα upon activation by Ca2+-bound CaM increases the distance between the two FPs in CaNARi.459 (B) The insertion of FPs into a receptor588 or between a GPCR and Gαs574 have been utilized to create FRET-based biosensors of receptor activation. (C) Biosensors for small G-protein activation utilize G-protein binding domains (e.g., Raf RBD, PKN RBD, EEA1 RBD), which bind to specific G-proteins upon activation to create a conformational change.627,636,642,644,646 (D) FRET-based biosensors for kinase activity consist of either an endogenous kinase substrate433 or a kinase substrate sequence paired with a phosphoamino acid binding domain474,484,507,512,526 sandwiched between two FPs, which undergo a conformational change upon phosphorylation. Conversely, the phosphatase biosensor CaNAR uses a fragment of NFAT1c, which is phosphorylated at basal levels and exhibits a conformational shift upon dephosphorylation by CaN.460 (E) Similarly, biosensors for other PTMs use substrates paired with protein domains that recognize the modified substrates. The Histac biosensors contain a full-length histone, H3 or H4, and a 11732


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Figure 4. continued fragment of a bromodomain (BRD)-containing protein that binds the acetylated substrate.650,651 Likewise, the OGT activity sensor is comprised of the O-GlcNAc binding GafD protein from E. coli which binds the O-GlcNAcylated substrate.654

protein to drive a change in FRET between an FP pair, analogous to the design of cameleon. For example, Mochizuki and colleagues constructed the Raichu (“Ras and interacting protein chimeric unit”) sensor by tethering full-length H-Ras to the Ras-binding domain (RBD) from Raf and sandwiching the resulting molecular switch between YFP and CFP (Figure 4C).636 This modular architecture has proven to be highly generalizable, and substituting H-Ras/Raf-RBD with other cognate binding pairs has yielded a family of Raichu sensors for Rap1,636 Rac,624 Cdc42,624 RhoA,644 RalA,634 TC10/RhoQ,647 and R-Ras648 (Table 1). Notably, while these biosensors play an important role in exploring the regulation of GTPases by GEFs and GTPase-activating proteins (GAPs), they cannot be used to study the specific regulation of Rho-family GTPases by guanosine dissociation inhibitors (GDIs) due to the masking of the GTPase C-terminus,684 leading to the development of sensors with reconfigured architectures. For instance, when constructing Raichu-Rab5, Kitano et al. kept the RBD between YFP and CFP but moved full-length Rab5a to the biosensor Cterminus.627 Others have opted to completely invert the design by sandwiching a FRET pair between the GTPase and RBD.641,642,646 Finally, some designs incorporate only the RBD, eschewing the inclusion of a full-length GTPase component in order to monitor endogenous GTPase activation.624,644,663 2.3.3.2.2.2. FRET-Based Enzyme Activity Reporters. Proteins must rapidly and continuously adapt their functions to suit the ever-changing needs of a highly dynamic cellular environment. Covalently altering protein sequences by introducing post-translational modifications allows cells to dynamically modulate virtually all aspects of protein function and greatly expand the functional diversity of the cellular proteome, and FRET-based biosensors have proven particularly apt for visualizing the activities of the enzymes responsible for catalyzing the myriad post-translational modifications taking place within cells. These biosensors are based on the incorporation of a target protein sequence into the sensing unit, which therefore functions as a surrogate substrate for a given enzyme activity. Molecular switching behavior is conferred either by utilizing a protein domain that intrinsically alters the biosensor conformation when modified by the target activity or by tethering a minimal substrate sequence (i.e., receiver domain) to a separate module that recognizes and binds the modified substrate (i.e., switch domain) to drive an activity-induced conformational change. Importantly, biosensors for monitoring enzyme activity are subject to enzymatic amplification, in that a single enzyme is able to react with multiple biosensors, which increases probe sensitivity due to the exponential relationship between the biosensor response and the number of active enzymes. This property, combined with the targetability of these genetically encoded probes to specific subcellular regions, allows FRETbased enzyme activity sensors to sensitively report on the spatiotemporal dynamics of endogenous enzyme activities within the context of their native regulatory environment in living cells. 2.3.3.2.2.2.1. FRET-Based Sensors for Monitoring Protease Activity. What can be considered the first genetically encoded

at its N- and C-terminus to a donor and acceptor FP, such that the activation-induced conformational change will alter FRET between the FP pair. This design has been applied to generate activation indicators for a variety of different enzymes, including a number of protein kinases. For example, to study the spatiotemporal regulation of Ca2+/CaM-dependent kinase II (CaMKII), a key signaling enzyme involved in learning and memory, Takao and colleagues appended YFP and CFP to the N- and C-termini of full-length rat CaMKIIα to generate Camuiα.458 The binding of Ca2+/CaM switches CaMKII from a closed, autoinhibited state to an open, active conformation, thus inducing a FRET decrease in Camuiα. Similar sensors have also been developed to probe Erk2 activation during MAPK signaling,478 the regulation of PKB/Akt activation by phosphoinositides and phosphoinositide-dependent kinase 1 (PDK1)-mediated phosphorylation,442 and the activation of PDK1 itself.499 Recently, the activation of the Ca2+/CaMdependent protein phosphatase calcineurin (CaN) was similarly studied by sandwiching the full-length CaN catalytic subunit between CFP and YFP459 (Figure 4A). Vilardaga and colleagues also previously utilized this biosensor strategy to investigate the activation kinetics of the α2A adrenergic receptor (α2AAR) and PTHR, members of two distinct classes of the GPCR superfamily of seven-transmembrane cell-surface receptors.588 To detect the conformational changes that occur during GPCR activation, the authors fused YFP to the C-terminus and inserted CFP within the third intracellular loop of both the α2AAR and the PTHR, as the activation of these GPCRs has been shown to include shifts in the relative position of the sixth transmembrane helix,682 which flanks the third intracellular loop (Figure 4B). In fact, several FRET-based sensor designs have been employed to study the activation of GPCRs, which comprise the largest family of signaling proteins and are responsible for mediating the activation of numerous signaling pathways in response to diverse extracellular cues. Activated GPCRs function as GEFs for heterotrimeric G proteins, leading to the dissociation of the GTP-bound α subunit (Gα) from the Gβγ-subunit dimer and downstream signaling. Thus, early studies utilized the decrease in intermolecular FRET between FP-labeled G protein subunits as a proxy for GPCR activation,577 an approach that continues to be used.683 Meanwhile, others have similarly monitored the association of FP-labeled receptor and the Gαβγ heterotrimer (e.g., ref 587 and also section 2.3.3.3). Recently, Malik et al. adapted this approach to devise a unimolecular GPCR activation sensor in which YFP and CFP, separated by a 10 nm ER/K linker (see section 2.3.3.1.4), are sandwiched between a full-length GPCR and a high-affinity binding peptide derived from a specific Gα subunit.576 An updated version of this design incorporates a full-length Gα subunit and permits association with endogenous Gβγ.574 Monomeric G proteins represent another large superfamily of signaling proteins that are crucial regulators of multiple cellular processes, and several FRET-based biosensors have been developed to track the activation dynamics of these small GTPases in living cells. In general, the design of these probes utilizes the intramolecular binding between a full-length GTPase and a tethered binding domain from an effector 11733


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family of FRET-based sensors for monitoring the activity of a multitude of different protein kinases that are too numerous to mention (see Table 1). Biosensor specificity is, of course, determined by the incorporation of a substrate sequence that will be recognized and phosphorylated by the kinase of interest but not by other kinases, while in some cases, additional sequence elements are included to enhance specificity, such docking sequences for MAPKs.474,475,490,496 Yet identifying a suitable, kinase-specific phosphorylation sequence can be a difficult task that often requires considerable trial and error, though more systematic approaches may help speed up this process, especially for kinases with poorly defined or unknown substrate preferences. For example, Tsou et al. utilized a positional scanning peptide library screen693 when designing a substrate sequence for their AMP-dependent kinase activity reporter (AMPKAR), although multiple candidate sequences were still tested.446 More recently, when designing a FRETbased KAR for Rho-associated protein kinase (ROCK), Li and colleagues initially trialed several previously characterized substrate sequences before turning to a newly developed kinase-interacting substrate screening (KISS) approach,694 in which cell lysates are run though kinase-coated beads to enrich interacting substrates, followed by tandem mass spectrometry to deduce the consensus phosphorylation sequence.521 Nevertheless, efforts to engineer a molecular switch can sometimes yield no successful candidates, in which case intrinsic molecular switches can also be used, as in the case of Zhou and co-workers, who utilized the native phosphorylationinduced conformational change in full-length 4EBP1 as the basis for their mTOR complex 1 activity reporter (TORCAR).433 It is important to remember that phosphorylation is a highly dynamic and reversable PTM where the addition of phosphate groups by kinases is constantly opposed by protein phosphatases which are responsible for catalyzing their removal. While cells typically contain a far smaller variety of protein phosphatases (e.g., mammalian genomes encode roughly 1/5th the number of phosphatases as kinases695), these enzymes are no less important in regulating protein function. Nevertheless, visualizing protein phosphatase activity in living cells has proven to be a far greater challenge compared with monitoring protein kinase activity. For one thing, these enzymes have differing requirements for substrate recognition: kinase specificity is defined by the residues that surround the target phosphorylation site, while phosphatases are far more promiscuous in this regard (a fact related to their smaller numbers), with specificity instead being governed by conserved docking motifs within substrate proteins696 and coordination by regulatory domains.697 The chief obstacle, however, is the fact that, unlike KARs, which become phosphorylated, a phosphatase sensor must already be phosphorylated, such that it can respond to being dephosphorylated, and constructing a constitutively “dephosphorylation-competent” molecular switch remains something of a conundrum. Thus far, the only design to successfully overcome this problem is that of the FRET-based CaN activity reporter (CaNAR),459,460 whose intrinsic molecular switch features an endogenous CaN substrate (nuclear factor of activated T-cells, NFAT) that is basally hyperphosphorylated by multiple kinases.698 Meanwhile, in the absence of a more generalizable design, the development of genetically encoded phosphatase sensors continues to lag far behind that of kinase sensors.

FRET-based biosensors were generated by sandwiching an oligopeptide containing a protease cleavage site between GFP and BFP, whereby catalytic cleavage of the protease substrate liberates the two FPs and produces a dramatic decrease in intramolecular FRET.685,686 Although these initial constructs were built largely as a proof of concept to explore and characterize the occurrence of FRET between different FP color variants, the site-specific cleavage of proteins by various proteases is in fact an essential component of numerous physiological, and pathological, processes, and this simple probe design has thus frequently been replicated to study the dynamics of a number of important cellular proteases. Cysteine-aspartyl proteases (caspases), for example, are key regulators of apoptotic cell death pathways, and several groups have studied the dynamics of both effector and initiator caspases by sandwiching the cleavage sites for caspase-3 (Casp3) (e.g., “DMQD” 5 7 0 or, more commonly, “DEVD”560−565,569), as well as Casp8 (“IETD”)565 and Casp9 (“LEHD”)569 between a FRET FP pair. Ouyang and colleagues have similarly visualized the activity of membrane type 1 matrix metalloproteinase (MT1-MMP), a membranetethered extracellular protease that is upregulated in invading cancer cells.572,573 Alternatively, Li et al. utilized full-length versions of the LC3B or GATE-16 proteins sandwiched between CFP and YFP to track the activity of Atg4, a key protease involved in regulating autophagy.559 2.3.3.2.2.2.2. FRET-Based Sensors for Monitoring the Dynamics of Protein Phosphorylation. The reversible phosphorylation of proteins on Ser, Thr, or Tyr residues is arguably the most important post-translational modification involved in regulating protein function in cells and also one of the most abundant, with ∼40% of the total human proteome estimated to be phosphorylated by protein kinases at some point in time.687 FRET-based genetically encoded kinase activity reporters (KARs) were designed based on the fact that cells contain multiple protein modules, known as phosphoamino acid binding domains (PAABDs), that are capable of specifically recognizing and binding phosphorylated residues within target proteins.688−690 Thus, by following the architecture established with cameleon, Zhang and colleagues engineered a bipartite kinase-inducible molecular switch in which a consensus phosphorylation sequence for PKA (LRRASLP, based on the so-called “kemptide” PKA substrate691) was tethered by a flexible linker to the PAABD 14-3-3τ.508 In the resulting first-generation “A-kinase activity reporter” (AKAR1),508 PKA-mediated phosphorylation increased the intramolecular binding between 14-3-3 and the substrate, thereby inducing a conformational rearrangement and altering the FRET between a flanking CFP and YFP pair.508 However, the high affinity of the 14-3-3 domain rendered AKAR1 insensitive to cellular phosphatases, and the largely irreversible FRET response was thus unable to capture the full temporal dynamics of PKA activity. Replacing 14-3-3τ with the lower-affinity forkhead-associated 1 (FHA1) domain, in conjunction with a modified PKA substrate (LRRATLVD) designed to better fit the preferred recognition sequence for FHA1,692 therefore yielded a second-generation AKAR (AKAR2), as well as subsequent variants (AKAR3506 and AKAR4504), with a fully reversible FRET response (Figure 4D).507 Much like the aforementioned Raichu GTPase sensor (section 2.3.3.2.2.1), the modular architecture of AKAR proved to be highly generalizable and has yielded a diverse 11734


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2.3.3.2.2.2.3. FRET-Based Sensors for Monitoring Other Post-Translational Modifications. Cells call upon an extensive repertoire of post-translational modifications to regulate protein function. This is perhaps best embodied by histone proteins, which comprise the core structural scaffold in chromatin and undergo a particularly high degree of posttranslational modifications along their N-terminal tails. These modifications provide recognition sites for various chromatinbinding proteins and also control access to DNA by the replication and transcriptional machinery, and understanding the dynamics of histone modification is therefore critical for unraveling the mechanisms of epigenetic regulation. Thus, Lin and Ting constructed a sensor for visualizing the phosphorylation of histone H3 at Ser28, which is catalyzed by numerous kinases,699 by engineering a molecular switch composed of the 30 N-terminal amino acids of histone H3 tethered to 14-3-3τ and sandwiched between CFP and YFP.484 Histones are also frequently acylated and methylated on lysine residues, and Lin et al. were able to generate a FRET-based biosensor for monitoring the methylation of histone H3 on lysine 9 or 27 by similarly using short peptides spanning these regions.653 However, more recent efforts have opted for designs that incorporate full-length histones, rather than just minimal peptides, including sensors developed to monitor the acetylation dynamics of both histone H3650 and histone H4.651,652 Again, these sensors feature engineered molecular switches wherein the specific histone is coupled to an appropriate binding domain to drive a conformational change (Figure 4E). As a critical component of these biosensors, chromodomains and bromodomains are compact protein modules that specifically recognize methylated700 and acetylated701 lysines, respectively. Thus, Lin and co-workers utilized chromodomains from HP1 polycomb proteins to recognize methylated histone H3,653 while Nakaoka et al. used the bromodomain from the BRD4 protein to construct a histone acetylation (Histac) sensor for histone H3 acetylation.650 In a newly engineered biosensor of histone H3 trimethylation on lysine 9, a chromodomain of HP1 was again utilized for recognizing methylated histone H3 and a full-length histone H3 was incorporated into the biosensor for high specificity,1001 similar to the H3 acetylation sensor Histac.650 Meanwhile, a unique form of glycosylation, involving the specific addition of O-linked N-acetylglucosamine (O-GlcNAc) to Ser or Thr residues in proteins throughout the cell, has emerged over the last several years as a major post-translational modification that is implicated in multiple physiological and pathological processes (recently reviewed in ref 702). In contrast to many established post-translational modifications, O-GlcNAcylation is exclusively regulated by a single pair of enzymes, namely, O-GlcNAc transferase (OGT), which catalyzes the addition of O-GlcNAc, and O-GlcNAcase, which catalyzes its removal. Furthermore, the reciprocal nature of O-GlcNAcylation and phosphorylation, which often compete for the same sites on many proteins, with OGlcNAcylation able to block protein phosphorylation, suggests a potentially important role for the dynamic interplay between these modifications in the regulation of intracellular signaling. Thus, to track the spatiotemporal dynamics of O-GlcNAcylation in living cells, Carrillo and co-workers set out to engineer an O-GlcNAc-responsive molecular switch and generate a FRET-based O-GlcNAc sensor.654 As the receiver domain, they utilized the OGT substrate sequence from casein kinase II, while the E. coli GafD protein, a monomeric lectin (i.e.,

carbohydrate-binding protein) that specifically recognizes OGlcNAc, was selected as the switching domain. Again, the selection of both a specific substrate sequence and an appropriate recognition domain is essential for designing an activity-induced molecular switch. Though currently limited to empirical trial and error, it is hoped that the application of more high-throughput methods, such as molecular evolution, will propel the wider development of FRET-based enzyme activity biosensors. 2.3.3.2.2.3. FRET-Based Sensors for Measuring Mechanical Forces in Cells. Cells are physical entities that engage in a variety of mechanical processes, both extrinsic (e.g., cell−cell/ cell-environment interactions) and intrinsic (e.g., chromosome segregation, cytokinesis). These processes generate mechanical forces that provide information on the physical state of the cell and its environment, sending signals to the intracellular biochemical machinery and regulating cell physiology and behavior. Indeed, mechanosensitive signaling is fundamental to virtually all aspects of biology across evolution.703 Elucidating the mechanisms by which cells sense and interpret stimuli from mechanical forces (i.e., mechanotransduction) is thus essential to our understanding of physiology and disease. Notably, the past decade has seen the development of several genetically encoded FRET-based biosensors capable of performing in situ measurements of the piconewton (pN)-scale mechanical forces experienced by individual molecules inside living cells. In general, these sensors make use of a tension-sensing module composed of an FP pair tethered to either side of a mechanosensitive element.230,233−236 This linker adopts a compact conformation at rest but becomes elongated under strain, thereby increasing the interfluorophore distance and causing FRET to vary as a function of mechanical tension across the module, which can be inserted within a protein of interest to visualize mechanical forces. For example, Grashoff et al. constructed a tension-sensing module using a molecular nanospring composed of 8 tandem 5-amino-acid repeats from the spider silk protein flagelliform,704 which they sandwiched between a FRET pair and inserted between the head and tail domains of the vinculin protein to measure tension across focal adhesions.236 Importantly, given the aforementioned mathematical relationship between distance and FRET efficiency (eq 1, section 2.2.2), these mechanosensing probes can be calibrated in vitro (e.g., using single-molecule force spectroscopy705) to convert quantitative FRET measurements into absolute pN force values. Performing such quantitative force measurements requires the mechanosensitive domain to fulfill certain requirements (reviewed in ref 706), such as relaxation upon the removal of force (i.e., reversibility) and a lack of hysteresis between elongation and relaxation. Furthermore, the identity of the mechanosensitive domain also determines the range of forces that can be detected. Thus, whereas the 40-amino-acid elastic domain used by Grashoff et al. was sensitive between 1 and 6 pN of force,236 Austen and co-workers utilized variants of the 35-amino-acid α-helical vilin headpiece peptide, which undergoes an unfolding transition in response to mechanical tension, to sense forces ranging from 7 to 10 pN.230 More recently, Brenner and colleagues found that a shorter flagelliform peptide comprising only 5 repeats showed a much broader sensitive range, spanning 2−11 pN.235 Nevertheless, these probes remain limited to monitoring intracellular forces within a relatively narrow window, while their FRET responses also exhibit somewhat poor dynamic ranges. 11735


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thus dynamic range. FRET can also be enhanced through the incorporation of tandem acceptor FPs,304 as demonstrated recently by Klarenbeek et al. in the development of fourthgeneration FRET-based cAMP sensors with dramatically improved dynamic ranges.302 The relative orientation of the donor and acceptor FPs also contributes to the FRET efficiency, as the donor and acceptor dipoles must be properly aligned for maximal energy transfer to occur. Although discussed only in the context of single-FP biosensors thus far, circular permutation can also be used to shift the angle at which an FP is fused to the sensing unit in a FRET-based biosensor. The resulting alterations to the relative orientation of the FP and its chromophore can have a significant impact on FRET efficiency by improving dipole− dipole alignment, which has been frequently used to increase biosensor dynamic range.287,309,373,425,506 Using truncated FPs (i.e., deleting the flexible N- or C-terminal regions) can also alter the orientation of the chromophore with respect to the final biosensor structure, as can modifying the linkers connecting the FPs to the sensing unit. However, absent detailed structural information, anticipating how specific changes will affect chromophore orientation is difficult, if not impossible, meaning that various incremental changes must be tested through trial and error. Furthermore, optimization typically involves pursuing multiple strategies and, given the above maze of options, may require testing dozens or even hundreds of candidates. More high-throughput approaches are thus sorely needed. To this end, Belal and colleagues developed a bacterial colony screening system for optimizing FRET-based KARs in which both kinase and sensor candidate are inducibly expressed from a single plasmid,436 a potentially important advance given that enzyme activity sensors are left out of current bacterial screening approaches. 2.3.3.2.4. Biosensors Based on Modulating Fluorescent Protein Dimerization. While controlling FP proximity to modulate FRET remains arguably the most popular reporting strategy for genetically encoded fluorescent biosensors, other designs have also emerged in recent years that utilize alternative fluorescent readouts based on FP proximity (also see section 2.4.3 below). For example, whereas most FP-based applications favor the use of engineered monomeric variants to avoid artifacts caused by the native propensity of FPs to selfassociate, Alford and colleagues have conversely sought to take advantage of the obligate oligomerization of RFPs, which stabilizes the chromophore and thus enhances brightness,160,710,711 in an effort to develop a novel biosensor reporting strategy based on modulating dimerization-dependent increases in RFP intensity.381 Starting with a weakly fluorescent, monomeric form of tdTomato,213 designated “A”, they performed extensive molecular evolution to identify a “dark” interacting partner, designated “B” and also derived from tdTomato, that would rescue the fluorescence of A upon heterodimer formation. This approach ultimately yielded a dimerization-dependent RFP (ddRFP) that exhibited an ∼10fold intensity increase upon dimer formation, which enabled the straightforward construction of dual-FP, single-color Ca2+, and protease activity sensors (Figure 5A).381 Subsequent engineering of this initial ddFP also succeeded in producing ddGFP and ddYFP variants.712 Meanwhile, Ding et al. recently observed that the B subunits, which contain no chromophore,381 were interchangeable between ddRFP-A (RA) and ddGFP-A (GA).307 This observation spurred the development of yet another novel biosensor readout based on so-called

Meanwhile, rather than relying on modulating the distance between a pair of FPs connected by an elastic linker, Meng and colleagues developed an alternative force sensor design that produces large FRET changes by altering the relative orientation between a pair of linked FPs whose dipoles are (almost) perfectly aligned at rest. 232 Such continued innovation will be crucial for expanding the utility of FRETbased force biosensors. 2.3.3.2.3. Strategies for Optimizing FRET-Based Biosensor Responses. Dynamic range plays a key role in determining the signal strength and detection limit of a genetically encoded fluorescent biosensor. In the case of FRET-based biosensors, this property refers to the degree of difference between the lowest FRET signal in the “off” state and the highest FRET signal in the “on” state; the larger this range, the more likely a small change in the parameter of interest will yield a robustly detectable FRET response. A substantial proportion of biosensor development is thus devoted to optimizing dynamic range, for which two major strategies include minimizing basal FRET in the “open” biosensor conformation and maximizing FRET efficiency in the “closed” conformation. The first strategy essentially entails increasing the separation between the donor and acceptor FPs and is largely accessible only to biosensors that feature an engineered, bipartite molecular switch rather than an intrinsic conformational change. For example, many such FRET-based biosensors feature a short linker between the receiver and switch domains (e.g., cameleon,377 AKAR,508 and Raichu636). However, in an effort to develop an optimized backbone for constructing FRETbased biosensors, Komatsu and colleagues found that progressively increasing linker length using repeating (SAGG) units yielded proportional decreases in basal FRET signals.441 This work yielded the “extension for enhanced visualization by evading extraFRET” (Eevee) system and the corresponding “EV linker”, along with a number of enhanced FRET-based biosensors.441 Conversely, FRET efficiency can be increased by minimizing the interfluorophore distance in the closed sensor conformation. Although this conformation is expected to bring the donor and acceptor FPs into close proximity, conformational dynamics within the sensor may cause this distance to fluctuate. Thus, allowing the FPs to associate may help ensure they achieve the closest possible approach. In fact, Vinkenborg et al.707 suggested this effect as the mechanism underlying the enhanced FRET behavior of the molecularly evolved CyPet/ YPet pair708 and later applied this principle to improve the dynamic range of the CALWY Zn2+ sensor.420 Given that CALWY exhibits a FRET decrease upon Zn2+ binding, FP dimerization thus helps stabilize the high-FRET “off” state but, importantly, does not affect the conformational switch or the resulting FRET change. Meanwhile, this approach may conceivably pose a problem in sensor designs where the conformational change brings the donor and acceptor closer together and thus increases FRET, as FP dimerization may yield spurious FRET increases, though previous work has suggested otherwise.304,709 Nevertheless, a more straightforward approach is simply to use better FPs. Recall from section 2.2.2 that R0, the Förster distance, is influenced by the photophysical properties of the donor and acceptor. These include the extinction coefficient (i.e., how efficiently photons are absorbed) and quantum yield (i.e., how efficiently photons are re-emitted), and selecting a donor and acceptor pair that optimizes these parameters can greatly improve FRET, and 11736


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exchange GA-B dimer formation for RA-B dimer formation in response to a biochemical signal, yielding reciprocal (i.e., ratiometric) changes in ddGFP and ddRFP intensity307 (Figure 5A). One potentially significant advantage of FPX is its apparent insensitivity to linker length/composition, suggesting a far more straightforward path for biosensor design than that for FRET-based sensors. 2.3.3.3. Biosensor Designs Incorporating Luminescent Proteins. Bioluminescence, wherein light is generated through an enzyme-catalyzed chemical reaction, represents an alternative biosensing strategy that has in some ways been overshadowed by the popularity of FP technology. Yet bioluminescence offers a number of advantages that support its application in areas where the use of fluorescence-based biosensors continues to pose a challenge (e.g., in vivo). Specifically, the wavelengths used to excite most genetically encoded fluorescent biosensors often cause problems related to autofluorescence, fluorophore bleaching, phototoxicity, and poor tissue penetration due to scattering/absorption within the specimen. In contrast, bioluminescence relies on a broad class of luminescent proteins, known as luciferases, that emit light via the oxidation of a luciferin substrate (e.g., coelenterazine or similar molecules) and therefore require no external illumination. Thus, luminescence-based readouts have the potential to yield reduced background and enhanced contrast and signalto-noise ratios and may also lower some of the barriers to in vivo imaging. Luciferases are present throughout the natural world, most notably in a veritable menagerie of marine organisms.713 Among the most commonly used luciferase variants are those derived from the sea pansy Renilla reniformis (Renilla luciferase, RLuc) and the North American firefly Photinus pyralis (firefly luciferase, FLuc), which have been used to construct genetically encoded biosensors that mirror many of the reporting unit configurations found in FP-base probes. For example, based on structural data indicating that FLuc comprises two globular domains that close in a hingelike motion upon substrate binding,714 Fan and colleagues generated a circularly permuted FLuc in which the native Nand C-termini were relocated to opposite sides of this hinge, such that the insertion of a sensing unit would render FLuc activity sensitive to various biochemical signals.318 Bridging this gap using either a short “DEVDG” peptide linker or the CNBD-B domain from PKA RIIβ thus yielded luminescent indicators for caspase-3 activity or cAMP accumulation, respectively318 (Figure 5B). Luciferases are also compatible with fragment complementation, offering a sensitive and dynamic readout that contrasts with the irreversible complementation of FP fragments. Indeed, Stefan et al. previously used this approach to monitor PKA activation in living cells by tagging the regulatory and catalytic subunits of PKA with complementary RLuc fragments.510 Herbst and colleagues similarly used RLuc fragment complementation to generate luminescent KARS in which the PAABD and substrate peptide are expressed as two separate polypeptides, fused to complementary RLuc fragments, which reassociate upon phosphorylation.503 This approach successfully yielded luminescent KARs for monitoring PKA and PKC activity (Figure 5B). Much like the excited state of a FP chromophore, the highenergy reaction product of luciferin oxidation is also capable of nonradiative energy transfer to a nearby acceptor. Thus, a number of genetically encoded biosensors utilize biolumines-

Figure 5. ddFP- and luminescence-based biosensors. (A) Bimolecular (top) or unimolecular (bottom) Ca2+ biosensor designs based on ddFPs.307,381 The dimeriaztion of the dim ddFP partner, FP-B, with either RFP-A or GFP-A increases their fluorescence, and this dimerization is modulated by the Ca2+-dependent binding of calmodulin to M13. (B) Modulating the structure of split luciferaces by conformational switches has been utilized in cAMP, PKA, and Ca2+ biosensors.290,318,503 In the FLuc cAMP sensor, the conformational switch induced by binding of cAMP to PKA RIIβ allows the firefly luciferase (FLuc) to properly form, thus allowing the enzyme to catalyze the degradation of luciferin and emit photons.318 Similarly, the Nano-lantern Ca2+ biosensor undergoes a Ca2+-dependent reconstitution of renilla luciferace (RLuc), which is then capable of BRET with the adjacent, brighter YFP.290

fluorescent protein exchange (FPX). Specifically, these biosensors utilize a molecular switch that is designed to 11737


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Figure 6. Coupled reporter systems. (A and B) Biosensors reporting the cell cycle phase of cells utilize fluorescent proteins fused to protein fragments that are selectively degraded during specific phases of the cell cycle.221,228 In the Fucci system, Gem is degraded during the G1 and late M phases and, conversely, Cdt1is degraded in S and G2 phases.228 The improved Fucci4 adds the condensation of chromatin around Histone H1 to report the M phase, as well as SLBP, which is degraded after S phase, in addition to labeled Cdt1 and Gem from Fucci.221 (C) The chimeric receptor BBD-ECat, which consists of an extracellular TrkB domain, which dimerizes upon binding BDNF, and an intracellular EGFR domain, is coupled with an EGFR activity reporter ECaus to act as a reporter for BDNF release by neurons.295 (D) Similarly, the expression of the M1 receptor and the Ca2+ biosensor TN-XXL408 in a cocultured reporter cell uses the endogenous coupling of the GPCR M1R to Gαq which, upon receptor stimulation, leads to an increase in intracellular Ca2+ to report the presence of a neurotransmitter, acetylcholine (Ach).530

of GCPR signaling.716 Galés et al., for instance, monitored intramolecular BRET between RLuc-tagged GPCRs and GFPtagged G proteins to probe GPCR activation,575 as well as the conformational dynamics of preassembled receptor-G protein complexes,586 and Thomsen et al. recently used a similar approach to probe the assembly of GPCR signaling components on the endosomal surface (also discussed in section 4.1).585 Meanwhile, Dacres and colleagues used intramolecular BRET between a C-terminal RLuc and GFP inserted within the third intracellular loop to directly monitor GPCR activation,589 similar to a previous FRET-based

cence resonance energy transfer (BRET) as the reporting mechanism, which often requires little more than replacing the donor FP in an existing FRET-based biosensor design with a luciferase. For example, Gulyás and co-workers constructed a BRET-based Ca2+ sensor by incorporating an enhanced version of RLuc715 in place of CFP in the D3 variant of YC.367 Over the years, this strategy has similarly yielded BRET-based sensors for monitoring cAMP accumulation,300,310 ERK activity,472 ATP concentration,285 membrane voltage,251 and ubiquitination649 (Table 1). BRET-based biosensor assays are also widely used to study the dynamics 11738


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design.588 Notably, the authors argued that BRET offered a more sensitive readout of GCPR activation compared with FRET.589 Nevertheless, luciferase- and BRET-based sensors have historically underperformed compared with fluorescent biosensors in terms of overall intensity,290 despite the fact that BRET can significantly enhance the light output of luciferase (e.g., up to 6-fold higher quantum yield in vitro for RLuc paired with GPF vs RLuc alone717,718). Poor energy transfer efficiency in most BRET-based probes is one likely explanation for this deficiency. Thus, Saito and colleagues set out to address this problem by developing the novel luminescent protein Nano-lantern.290 Nano-lantern comprises an optimized RLuc fused directly to a bright YFP variant (Venus374) and is designed in such a way as to maximize intramolecular BRET. Indeed, at roughly 10 times the brightness of RLuc alone, Nano-lantern was suitable for both live-cell and in vivo imaging. Furthermore, Nano-lantern facilitated the construction of novel luminescence-based biosensors in which a molecular switch is used to modulate Nano-lantern intensity through RLuc fragment complementation, as opposed to direct modulation of BRET efficiency, yielding sensors for Ca2+, cAMP, and ATP (Figure 5B).290 In addition, while some luciferase color variants have been generated through protein engineering,719−722 the color spectrum of Nano-lantern can be readily altered by incorporating different FPs as the BRET acceptor.397,398 Notably, Suzuki et al. recently developed 5 color variants of a brighter, enhanced Nano-lantern397 that also incorporates the highly optimized NanoLuc variant of Oplophorus gracilirostris luciferase.723 Although they have yet to be widely adopted in biosensor design, these and other bright luminescent proteins, such as the recently reported Antares724 and Akaluc,725 may be poised to fulfill the promise of bioluminescence-based in vivo imaging of dynamic cellular processes. 2.3.4. Coupled Reporter Systems. Although genetically encoded biosensors are primarily designed to monitor the dynamics of discrete biochemical events, designs that couple the actions of related biochemical processes can provide spatiotemporal information on complex cellular phenomena, thus greatly expanding the utility of these molecular tools. For example, the reciprocal oscillations of the APCCdh1 and SCFSkp2 E3 ubiquitin ligase complexes mark different cell cycle phases,726 and Sakaue-Sawano et al. previously applied this knowledge to generate a “fluorescent ubiquitin-based cell cycle indicator” (Fucci) based on the APC Cdh1 and SCFSkp2 substrates Geminin and Cdt1, which accumulate in G1 and S/G2/M, respectively.228 Thus, fusing Cdt1 or Geminin fragments to monomeric Kusabira Orange 2 (mKO2) or monomeric Azami Green (mAG) converted the reciprocal nuclear accumulation of orange and green fluorescence into a high-contrast indicator of cell cycle dynamics powerful enough to enable the in vivo monitoring of cell cycle progression226,228,727 (Figure 6A). Fucci has also enabled detailed examinations of the relationships between the cell cycle and processes such as transcriptional activity728 and mitochondrial dynamics.729 Meanwhile, several modifications and improvements to Fucci have also been reported to further extend its utility. For instance, Sakaue-Sawano et al. improved the color contrast of Fucci by replacing mKO2 and mAG with mCherry and mVenus, yielding Fucci2225, and more recently developed an alternative indicator system, Fucci(CA), that utilizes changes in the activities of the CUL4Ddp1 and APCCdh1 E3

ligases to indicate cell cycle transitions during interphase (i.e., G1, S, and G2).220 Notably, Bajar and colleagues also recently reported the construction of Fucci4, which utilizes the periodic degradation of 4 spectrally distinct FP-tagged proteins to monitor each individual phase of the cell cycle221 (Figure 6B). Several cell-based indicator systems have also been developed that couple the detection of signaling molecules secreted by one cell to a FRET-based biosensor response in a second cell, thereby providing spatiotemporal information on the dynamics of intercellular rather than intracellular signaling, particularly among neurons. For example, Sato et al.603 developed a reporter system for detecting secreted nitric oxide (NO) based on PK15 cells that stably express the cGMP sensor CGY.327 Importantly, PK15 cells endogenously express soluble guanylate cyclase (sGC), which selectively generates cGMP in response to NO binding.730 Thus, sGC couples NO detection to the CGY FRET response, with the resulting Piccel (“PK15 indicator cell”) system able to detect picomolar NO release by cocultured hippocampal neurons.603 Using a similar principle, Nakajima and co-workers also developed a “BDNF sensor cell” (Bescell) indicator system that couples BDNF binding by a chimeric receptor tyrosine kinase (BDNF-binding domain fused to EGFR kinase domain) with a FRET-based EGFR activity reporter295 (Figure 6C). Likewise, taking advantage of GPCR coupling to Gαq-containing G proteins, which activate PLC to trigger IP3 production and ER Ca2+ release, Nguyen and colleagues developed a similar approach to visualize endogenous neurotransmitter release using “cellbased neurotransmitter fluorescent engineered reporters” (CNiFERs).530 This system, which utilizes HEK293 cells stably expressing a given metabotropic neurotransmitter receptor along with the high-performance GECI TN-XXL,408 has been used to image the in vivo dynamics of cholinergic, dopaminergic, and noradrenergic signaling in mouse brains530,532 (Figure 6D). Thus, the modularity of these designs holds the potential to illuminate the spatiotemporal dynamics of a broad range of intercellular signals. 2.4. New Biosensor Classes

Years of tireless innovation have spawned a verdant landscape of genetically encoded fluorescent biosensors capable of visualizing the spatiotemporal dynamics of a diverse and expansive, though by no means exhaustive, array of biochemical, biophysical, and cellular phenomena. Meanwhile, new biosensor designs continue to emerge, often based on novel reporting strategies or biosensor configurations, that not only fill gaps in the existing toolkit but also open up entirely new avenues for exploring previously unanswerable biological questions, thus promising to redefine the reach of these powerful molecular tools. 2.4.1. Sensors Based on Infrared Fluorescent Proteins. Decades of engineering have yielded GFP-like FPs in nearly every color of the rainbow.7 Notably absent, however, have been variants that excite and emit between 650 and 900 nm (i.e., the so-called near-infrared [NIR] window731), wavelengths that are preferable for both cellular and wholebody in vivo fluorescence imaging for the reasons enumerated above (e.g., autofluorescence, phototoxicity, absorbance, and scattering). Yet multiple light-absorbing proteins are present in nature that derive their chromophores from the covalent incorporation of endogenous cellular cofactors,732 and several bacterial phytochromes (BphPs), which utilize the heme catabolic intermediate biliverdin (BV) as their chromophore, 11739


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Nevertheless, the development of IFP has ignited a flurry of efforts to develop other alternative FPs, some of which display comparable brightness to that of EGFP.222 These advances promise to yield additional novel biosensor designs beyond those currently possible. 2.4.2. Biochemical Activity Integrators As in Vivo Snapshot Reporters. The idea of a “snapshot” reporter was originally conceived as a way to get around the daunting challenge of performing three-dimensional whole-brain imaging of neuronal activity in order to delineate causal relationships between the firing of specific neuronal subsets and specific behaviors or stimuli (i.e., brain activity mapping).741 Thus, instead of continually reporting the dynamics of neuronal activity (e.g., Ca2+ elevations), the snapshot reporter integrates, or “memorizes”, neuronal activity over an arbitrarily defined period of time by generating a stable signal only in the presence of both activity (e.g., Ca2+) and an external trigger such as light, thereby enabling detailed, retrospective analyses of neuronal firing patterns. Recently, Fosque and colleagues were able to develop one such snapshot reporter using the single-FP Ca2+ indicator GCaMP as a template.379 Specifically, based on a previous report detailing the construction of GR-GECO, a single-FP GECI whose fluorescence emission switches color from green to red upon illumination with 400 nm light,395 they set out to similarly engineer a photoconvertible GECI, albeit one whose photoconversion efficiency was modulated by the Ca2+dependent molecular switch, such that the change in emission wavelength occurs only in response to both Ca2+ and 400 nm light. This was achieved by incorporating a circularly permuted variant of the green-to-red photoconvertible FP mEos2742 into the GCaMP backbone in place of cpEGFP, after which molecular evolution was performed to generate CaMPARI, a calcium-modulated photoactivatable ratiometric integrator379 (Figure 8). During 400 nm illumination, the green fluorescent signal from CaMPARI rapidly changes color to red in proportion to the concentration of Ca2+; thus, the ratio of red-to-green fluorescence intensity provides a permanent snapshot of neuronal activity during the recording period established by the length of illumination.379 Zolnik and coworkers also recently demonstrated that the photoconversion rate of CaMPARI can be adjusted by modulating the intensity of 400 nm illumination, thereby tuning the sensitivity of CaMPARI to different thresholds of neuronal activity.743 Importantly, CaMPARI also behaves as a traditional single-FP GECI, with the intensities of both the green- and redfluorescent states decreasing strongly in response to Ca2+379, thereby providing a crucial internal control for any possible effect that the photoconversion light itself may have on the system under investigation. Alternatively, a pair of studies365,366 published within the past year have reported the development of snapshot reporters that hew more closely to the original design first articulated by Roger Tsien.741 In this scheme, the extrinsic (e.g., light) and intrinsic (e.g., Ca2+) signals are integrated to drive a transcriptional reporter that permanently labels active neurons with high spatiotemporal resolution for post hoc analysis. Thus, although differing somewhat in their particulars, both the Cal-Light system designed by Lee and colleagues366 and the “fast light- and activity-regulated expression” (FLARE) system devised by Wang and co-workers365 feature a threecomponent, dual-molecular-switch design that functions as an “AND gate” to control the light- and Ca2+-dependent release of

have been demonstrated to emit photons in the 600 to 700 nm range.733−735 BphPs are multidomain photoreceptors that transduce light into signaling activity,736 and Shu and colleagues were able to successfully engineer the first infrared fluorescent protein (IFP) based on a pared-down version of the Deinococcus radiodurans BphP containing just the chromophore-binding GAF and PAS domains.737 Intriguingly, the unique topology and chromophore-binding interactions of IFP are enabling the development of novel biosensors strategies that are not feasible using GFP-like FPs. For example, although numerous genetically encoded fluorescent protease sensors have been developed over the years (see sections 2.3.3.2.2.2.1 and 2.3.3.2.4), no approach has succeeded in producing a high-contrast, fluorogenic biosensor, wherein the cleaved sensor gains fluorescence. Thus, To and colleagues set out to use IFP as an alternative scaffold for constructing such a reporter.568 In BphP, and thus IFP, chromophore formation occurs through the insertion of BV into a binding pocket within the GAF domain, followed by the autocatalytic formation of a thioether bond with a conserved cysteine residue.736,738 Noting the proximity of this residue to the BV binding pocket in IFP, To et al. devised a way to control IFP chromophore formation by modulating the distance between the binding pocket and catalytic cysteine within a circularly permuted IFP (cpIFP).568 The original Nand C-termini of IFP were truncated and tethered using a short linker containing a protease cleavage site to physically constrain the catalytic cysteine such that chromophore formation only occurs following cleavage of the linker (Figure 7). Meanwhile, to ensure that cpIFP remained intact following

Figure 7. Infrared FP-based caspase-3 reporter, iCasper. The introduction of the caspase-3 cleavage sequence into the circularly permuted mIFP prevents the incorporation of BV into the GAF domain, but cleavage by caspase-3 liberates the catalytic cysteine to promote the incorporation of BV and the formation of the chromophore.568

cleavage, splitGFP was added to the new termini introduced between the PAS and GAF domains. This approach yielded a fluorogenic infrared fluorescent caspase reporter (iCasper) for monitoring caspase-3 activity, which allowed the authors to highlight apoptotic cells during morphogenesis and tumor development in Drosophila.568 One potential concern regarding the wider use of BphPderived IFPs is their somewhat poor quantum yield and low brightness, which may be further exacerbated by low BV concentrations in certain organisms and cell types.739,740 11740


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transgene can be placed under the control of the snapshot reporter, thereby facilitating precise functional manipulation of labeled neurons. For instance, the coexpression of a channelrhodopsin was used to permit the targeted inhibition 366 or reactivation 365 of neurons that had been fluorescently labeled by the snapshot reporter, an ability that is crucial for identifying those neurons whose firing bears a true causal link with a given behavioral response.741 Furthermore, Lee and colleagues also developed a similar system for identifying and manipulating neurons that respond to specific neuromodulators by replacing the calmodulin-M13 switch with one based on GPCR-βarr binding,578 highlighting the ability of these modular designs to be reconfigured as snapshot reporters for other biochemical events, with the potential for numerous applications beyond neurobiology. 2.4.3. Fluctuation-Based Biosensors. The biochemical pathways that mediate intracellular signaling are increasingly understood to operate within an exquisitely controlled spatial framework, or activity architecture, that is established through various modes of compartmentalization (discussed in section 4). Because much of this compartmentalization is ultimately governed on a molecular scale, such as through the assembly of multiprotein signalosomes, genetically encoded fluorescent biosensors capable of visualizing and localizing biochemical activities on a comparable spatial scale are extremely desirable. That being said, a number of super-resolution imaging modalities have been developed that rely on FP photochromism, or the ability of certain FPs to stochastically and reversibly switch between a bright, fluorescent state and a dark, nonfluorescent state under specific illumination conditions (reviewed in ref 25). FP-based biosensors engineered to modulate this photochromic behavior in response to a biochemical input thus represent one potential strategy for imaging the precise locations of biochemical activities. Photochromism is thought to involve the disruption of molecular contacts between the FP chromophore and the surrounding β-can, leading to increased conformational flexibility and decreased fluorescence emission.25 In addition to exhibiting controlled on/off switching, photochromic FPs also display stochastic intensity fluctuations (i.e., blinking), and Mo et al. recently found that close proximity between the photochromic green FP Dronpa746 and the red FP TagRFPT165 induced significant fluctuations in TagRFP-T fluorescence intensity.477 These intensity fluctuations, which were specifically induced by Dronpa proximity, did not require the Dronpa chromophore but were dependent on surface residues in the Dronpa β-can, suggesting that physical contact with Dronpa deforms the TagRFP-T β-can and directly increases its intrinsic207 blinking behavior. Further analysis revealed that this phenomenon, termed “fluorescence fluctuation increase by contact” (FLINC), was sensitive to the intermolecular distance between Dronpa and TagRFP-T within the range of 5−6 nm, thus lending itself to the construction of a novel class of proximity-based biosensors compatible with super-resolution imaging.477 Using FRET-based biosensors as a template, Mo and colleagues substituted the FRET FP pair in AKAR3EV441 with Dronpa and TagRFP-T (Figure 10). When combined with the fluctuation-based super-resolution imaging approach pcSOFI,27 the resulting FLINC-AKAR1 sensor was able to map dynamic changes in PKA activity with nanometer precision, facilitating the detailed dissection of PKA compartmentalization477 (see additional discussion in section

Figure 8. Photoconversion-based snapshot reporter of Ca2+. CaMPARi acts similarly to other green single color Ca2+ biosensors where the green fluorescence intensity is dependent on the Ca2+ concentration (left), except it can also “record” the presence of high Ca2+ during a snapshot in time. When the CaMPARi biosensor is illuminated with blue/violet light and the intracellular Ca2+ is high, the biosensor will irreversibly convert to a red (right) Ca2+ biosensor.379

a membrane-tethered transcription factor, namely, the tetracycline-inducible transcriptional activator (tTA) (Figure 9). First, these designs utilize a light-dependent molecular switch composed of the light-oxygen-voltage (LOV) domain and C-terminal Jα-helical extension (Jα) from Avena sativa phototropin, wherein blue-light absorption by the flavincontaining LOV domain causes the C-terminal Jα helix to unwind,744 which can be used to unmask a specific peptide sequence in response illumination,745 in this case a cleavage site for the tobacco etch virus protease (TEVp). Meanwhile, the second, Ca2+-dependent molecular switch utilizes calmodulin binding to the M13 peptide to modulate cleavage of the TEVp substrate, either by controlling the fragment complementation of N- and C-terminal portions of TEVp366 or by simply recruiting TEVp to the cleavage site.365 Hence, only the combined presence of both light (to define the recording window) and Ca2+ (in the form of neuronal activity) will permit cleavage of the TEVp substrate and release of the tethered tTA domain to drive the expression of a reporter construct in the nucleus (Figure 9). Much like CaMPARI, both of these methods enabled active neurons to be specifically and selectively labeled with a fluorescent signal (i.e., FP expression) within the span of the illumination window.365,366 Importantly, however, a key advantage of these transcription-based readouts is that any 11741


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Figure 9. Transcription-based snapshot reporters. The Cal-Light and FLARE Ca2+ biosensors create an “AND gate” design by combining the optogenetically controlled AsLOV2 domain with a Ca2+ switch controlled split-TEV protease, which leads to the cleavage of the tTa-VP16 transcriptional activator only in the presence of high Ca2+ and blue light.365,366 The cleavage of the transcriptional activator will then turn on the expression of a reporter gene such as GFP.

4.3.1). These initial studies hint at the potentially transformative impact of this nascent biosensing strategy. Genetically encoded fluorescent biosensors have evolved into a remarkably diverse set of molecular tools capable of probing a broad range of cellular states and biochemical processes. In the following sections, we delve further into the application of genetically encoded fluorescent biosensors by focusing our attention on how this technology has been leveraged to extract detailed information on the temporal and spatial regulation of the intracellular biochemical machinery and thereby elucidate the molecular logic underlying signal transduction networks. Figure 10. Fluctuation-based PKA biosensor FLINC-AKAR1. Fluorescence of TagRFP-T is modulated by its interaction with Dronpa through a process termed FLINC, such that when in close proximity to Dronpa, TagRFP-T exhibits increased fluorescence fluctuation. This enables this biosensor to report PKA activity at a subdiffraction spatial resolution477 through the use of the superresolution technique pcSOFI.27

3. OBTAINING TEMPORAL INFORMATION FROM BIOSENSORS The ability of cells to dynamically respond to a vast array of environmental changes and modes of cellular communication has arisen from the evolution of a highly connected network of fairly simple chemical reactions.747 Characterizing the dynamics of these biochemical pathways is therefore essential 11742


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Figure 11. Temporal dynamics quantified by fluorescent biosensors. (A) The kinetics of GPCR signaling. First, the receptor undergoes a conformational change in response to ligand binding, which was observed to occur with a time constant on the order of 40 ms in the α2-Adrenergic Receptor using the biosensor α2-AR-CAM.588 Next, the heterotrimeric G protein associates with the activated receptor. A2A Adenosine Receptor, A2AR, fused with YFP at the C-terminal tail and CFP-labeled Gγ2 exhibited an increase in FRET upon stimulation with adenosine with a time constant of approximately 50 ms.584 Finally, the receptor stimulates the exhange of GTP for GDP on the Gα subunit, thus activating Gα and promoting the dissociation of Gα from Gβγ. Biosensors consisting of a CFP-labeled Gγ subunit and either Gαi581 or Gαs584 fused to YFP showed activation time constants on the order of 500 ms. (B) Adaptation is the return of a signaling pathway toward its previous state while under continued stimulation. PC12 cells expressing the ERK biosensor EKAR show an adaptive response to EGF stimulation (gray lines) but a much more sustained response to NGF stimulation (black lines), adapted with permission from ref 754. Copyright 2011 American Society for Microbiology. Negative feedback by ERK onto Raf activation is hypothesized to lead to a transient response, whereas positive feedback in the NGF signaling pathway is hypothesized to lead to bistability.755 (C) Oscillations are the regular or semiregular cycling between activity/concentration states, as can be seen in the TEA-induced Ca2+ and PKA activity oscillations in MIN6 cells. These oscillations were observed through the simultaneous measurement of Ca2+ concentration with the Ca2+ dye Fura-2 (black line) and a Green/Red variant of AKAR, GR-AKAR, (red line) at the single-cell level in MIN6 cells.308 While the inhibition of K+ channels by ATP and the interplay between voltage and Ca2+ are the primary driver of the Ca2+ oscillations, the negative feedback of Ca2+ through cAMP and PKA is hypothesized to strengthen these oscillations and tune their frequencies. Adapted with permission from ref 308. Copyright 2011 Springer Nature. (D) Bistability and ultrasensitivity describe phenomena wherein a signaling pathway is insensitive to stimulation below a certain threshold dose but responds in a switchlike fashion to superthreshold stimuli. HeLa cells expressing the JNK biosensor JNKAR1 did not respond to anisomycin at concentrations of 20 (cyan ◆), or 50 (blue ▼) nM, but exhibited a strong response to 500 nM (red ●) and 5 μM (black ■). Adapted with permission from ref 490. Copyright 2010 National Academy 11743


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Figure 11. continued of Sciences. In addition to the multistep activation along the JNK signaling pathway, positive feedback by JNK onto the activation of upstream regulator MKK7 is hypothesized to contribute to the ultrasensitive nature of activation by anisomycin.756

intracellular loop and intracellular tail, respectively, of the PTHR and α2AAR.588 These biosensors showed that the kinetics of the conformational change within the receptor could vary significantly, where PTHR is activated much more slowly than α2AAR, with time constants of 1 s and less than 40 ms, respectively.588 After this conformational change occurs, the receptor can then couple with its cognate heterotrimeric G proteins. Quantification of the kinetics up through this step was done by labeling Gγ with CFP and inserting YFP into the intracellular tail of the adenosine receptor A2A (A2AR) and β1adrenergic receptor (β1-AR).584 This association between the receptor and G proteins occurred rapidly where these biosensors both exhibited a time constant near 50 ms.584 Finally, this association of the heterotrimeric G protein with the receptor stimulates the exchange of GDP for GTP on the Gα subunit and dissociation of Gα from the Gβγ subunits. Biosensors to study this final step were developed by fusing a Gα subunit and a Gβ or γ subunit with YFP and CFP, respectively.581,584 These studies showed that the activation of the Gα subunit was most likely to be rate limiting, where Gαi1 showed a t1/2 of approximately 700 ms in response to an α2A adrenergic agonist581 and Gαs exhibited a time constant near 500 ms in response to A2AR and β1-AR agonists.584 The responses from these biosensors were measured at frequencies on the order of 20−75 Hz, which was essential to be able to quantify the kinetics of these rapid inter- and intramolecular changes. Given that Gα, Gβ, and Gγ comprise 23, 5, and 12 different isoforms, respectively, exhaustively testing all of these combinations may be infeasible, but genetically encoded fluorescent biosensors make it possible to test specific combinations.580 For example, Gibson and Gilman examined the differences between Gαi isoforms 1, 2, and 3 and Gβ isoforms 1, 2, and 4, showing that Gαi1 exhibited the greatest FRET response to α2-adrenergic receptor stimulation when coupled with Gβ1 or Gβ4 subunits but not Gβ2.580 This type of work can provide evidence of subunit isoform preference or specificity and help understand how cell-type-specific isoform expression can affect the response of GPCRs.750 Similarly, the use of receptor-specific agonists/antagonists with these biosensors can reveal differences in the strength and rate of activation of different G protein isoforms for each receptor.579,582,584 Since GPCRs are the first step in many signaling pathways, understanding the kinetics of their activation can help identify rate-limiting steps in a signaling pathway and evaluate potential mechanisms of pharmacological perturbation. These biosensors exemplify the benefits of using genetically encoded fluorescent proteins to study the kinetics of activation because they are able to examine the dynamics of specific proteins within the cellular context at a high temporal resolution. In addition to being able to examine the activation of proteins and protein complexes, many biosensors have been developed to evaluate the kinetics of signaling enzyme catalysis. Downstream of the Gαs- and Gαi -mediated regulation of cAMP production is the prototypical kinase PKA. PKA is a tetramer consisting of two regulatory subunits and two catalytic subunits, where binding of cAMP to the regulatory subunits unleashes the catalytic subunit from

for determining how signaling networks are organized and understanding how cells process external stimuli and make decisions about their fate. Traditionally, quantifying the kinetics of signaling reactions has relied on the use of various in vitro methods such as Western blotting or enzymatic assays. Although these methods remain important tools for studying intracellular signaling, genetically encoded fluorescent biosensors offer a number of advantages that make them instrumental for elucidating the temporal dynamics of signaling networks. In particular, because they are genetically encoded, these biosensors can be used to quantitatively track the kinetics of signaling molecules within the native cellular environment. Furthermore, thanks to the rapid, nanosecond time scale of fluorescence, the relatively high brightness of FPs, and the sensitivity of modern digital imaging equipment, the signals from fluorescent biosensors can be monitored with a much higher temporal resolution than is generally achievable through other in vitro methods. As such, fluorescent biosensors are powerful tools for studying the dynamics of signaling networks across a wide range of time scales. Below, we discuss the types of temporal dynamics that have been observed in cellular signaling and how biosensors have been instrumental in studying these dynamics. 3.1. Kinetics of Individual Reactions

Signaling networks are principally driven by changes in the concentrations of signaling molecules and the reaction rates of signaling enzymes. The most foundational understanding of temporal dynamics can be achieved by identifying the activation and catalysis rates for signal transduction. Historically, these have been studied in purified systems in an in vitro setting. While these assays are able to provide detailed and accurate measurements of the reaction rates of a specific signaling enzyme, they lack the ability to evaluate the dynamics and kinetics of all of the preceding steps that lead to the activation of that enzyme and any other regulatory mechanisms occurring within the cell. Genetically encoded fluorescent biosensors are able to quantify the kinetics of signal transduction in real time in a living cell. Quantifying these kinetics is the first step to understanding how the connected enzymatic reactions within a network can create the highly dynamic and complex signaling responses seen in nature. One example of fluorescent biosensors being used to study the activation of signaling proteins has been in the study of GPCR activation kinetics. GPCRs are one of the most drugtargeted protein families,748 therefore quantifying the kinetics of their activation is of interest for both understanding their physiological function and evaluating the effects of different drugs. GPCRs consist of a seven-transmembrane receptor that couples to a heterotrimeric G protein comprising α, β, and γ subunits.749 Ligand binding by the receptor causes a conformational change that promotes the activation of the Gα subunit, which is primarily responsible for downstream signaling, and is canonically considered to dissociate from the Gβγ subunits.749 There have been several fluorescent probes developed to quantify the kinetics of different aspects of GPCR activation (Figure 11A). First, the activation of the receptor was quantified by inserting CFP and YFP into the third 11744


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signaling enzyme activates two different pathways that converge on a downstream effector but have opposing effects,760 are key motifs involved in signaling adaptation. While these network motifs are needed for transient signaling dynamics, their existence alone does not guarantee that adaptive signaling will occur; in fact, negative feedback can lead to oscillatory dynamics instead. Adaptation is by definition a short-lived event which has been found to be susceptible to cell-to-cell variability; therefore, fluorescent biosensors have been critical to observing and understanding these transient dynamics. While these biosensors measure the kinetics of individual reactions or second messenger concentrations, the ability to observe how a given signaling network regulates signaling dynamics over time can provide insights into how the networks architecture can shape the observed response. For example, genetically encoded fluorescent biosensors have been instrumental in identifying how different stimuli can lead to either sustained or adaptive dynamics of extracellular-signal regulated kinase (ERK) signaling. These temporal dynamics are functionally important because the duration of ERK activity has been shown to regulate different cell-fate outcomes in the neuroendocrine PC12 cell line.496,755 Earlier in vitro methods measuring ERK activity in response to EGF or NGF had observed that stimulation with EGF resulted in transient ERK activity, whereas NGF stimulation induced a sustained response.761,762 These differences in ERK temporal dynamics also correlated with the fact that EGF promoted proliferation while NGF led to PC12 cell differentiation.761,762 The desire to obtain a more detailed temporal understanding motivated the development of fluorescent biosensors of both ERK activation (e.g., Mui2)478 and ERK activity, such as EKAR and subsequent improved variants474 (Table 1). When directly comparing the dynamics of ERK activity in response to EGF and NGF stimulation in PC12 cells, Herbst and colleagues were able to use EKAR to accurately calculate a t1/2 for ERK phosphorylation reversal that not only agreed with previous studies (Figure 11B) but also revealed that PKA activity modulated the adaptive signaling dynamics of ERK.503 Later work by Ryu et al., using an improved ERK biosensor, EKAR2G,473 examined the cell-to-cell variability of ERK dynamics in response to EGF and NGF.755 They observed that while the population averages showed a more transient response to EGF than with NGF, there was significant variability between cells. This variability convolutes what uniquely regulates these two signaling cascades, but they found that using a short pulse of growth factor stimulation, instead of sustained stimulation, yielded more uniform responses for both EGF and NGF. Using this experimental design, they observed that the EKAR2G response to low-dose NGF stimulation exhibited similar transient responses as low-dose EGF stimulation. But at high doses of NGF, a subset of cells exhibited a sustained response that was not observed with high dose EGF stimulation, suggesting that the NGF signaling pathway may exhibit bistability, wherein high doses of NGF lead to a switchlike, instead of adaptive, behavior (bistability discussed more in section 3.2.3). This evaluation of cell-to-cell variability is only possible through the use of fluorescent biosensors because these quantitative measurements of signal transduction can be performed at the single-cell level. Furthermore, this detailed analysis was coupled with a computational model of ERK signaling, which demonstrated that repeated pulses of either EGF or NGF could be used to approximate either a transient or

inhibition by the regulatory subunit. The temporal dynamics of PKA have been the focus of many studies due to its importance in several physiological processes and its utilization of scaffold proteins to coordinate spatiotemporal dynamics.751,752 The development of AKARs (section 2.3.3.2.2.2.2) enabled more detailed and specific studies of these signaling dynamics.508 First, AKAR1 was able to show the different temporal kinetics of stimulating PKA activation either directly through the activation of ACs by forskolin or indirectly through the activation of the Gαs linked β2-adrenergic receptor agonist isoproterenol. The temporal resolution of this early biosensor was a great improvement over what was possible through previous in vitro kinetic assays. Furthermore, biosensors have played an important role in evaluating the effects of signalosomes, coordinated complexes of interconnected signaling proteins, on enzyme kinetics. For example, A-kinase anchoring proteins (AKAPs), a family of scaffold proteins that bind PKA, coordinate the interaction of PKA with downstream substrates and upstream regulators of PKA activity, thus affecting both PKA activity and the kinetics of phosphorylation by PKA.753 This effect was directly observed by genetically fusing a PKA regulatory subunit directly to AKAR1, thus mimicking a scaffolded enzyme−substrate complex, which resulted in a nearly 2-fold faster response.508 In addition to modulating the temporal dynamics of signal transduction, scaffold proteins control the spatial architecture of signal transduction by anchoring signaling proteins to specific subcellular microdomains. The effects of scaffold proteins on both spatial and temporal dynamics, and the role of biosensors in studying them is discussed further in section 4.3. The capability of fluorescent biosensors to measure specific enzymatic activities within the cellular environment at a high temporal resolution has made them powerful tools for quantifying the kinetics of signaling protein activation and enzyme catalysis. 3.2. Higher-Order Signaling Dynamics

The balance between second messenger production and degradation and signaling enzyme activation and inactivation within signaling networks enable and define the complex dynamics observed within cells. The highly connected nature of signaling networks means that a very diverse array of dynamics can be observed, but these dynamics can be understood in terms of a small set of basic building blocks of signaling dynamics that are defined by how the network is connected.757 Furthermore, the organization of the network connections can be grouped into similar topologies, which are called signaling motifs.758 Here, we will briefly introduce some of the more common signaling dynamics observed and some network motifs that lead to them, but there are several detailed reviews discussing the details of signaling network topology and their effects on signaling dynamics.757−759 3.2.1. Adaptive Responses. One common signaling dynamic observed is what is referred to as adaptation or a transient response. Adaptation can be defined as the resetting of a cellular signaling level to its previous state after stimulation760 (Figure 11B). This type of signaling dynamic can be important for cells to be able to respond to changes in stimulation levels instead of the absolute level of the signal. Previous work has identified that negative feedback loops, where the activation of a downstream signaling enzyme will lead to the inhibition of one or more of its upstream regulators, and incoherent feed forward loops, where an upstream 11745


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oscillatory fashion.772 The high temporal resolution of fluorescent biosensors makes it possible to study the kinetics of this oscillatory behavior, such as the frequency and magnitude of the oscillations.772 These tools helped determine that the primary mechanism responsible for triggering these Ca2+ oscillations is the increase in the ATP/ADP ratio, due to glycolysis. This increase in ATP in turn inhibits ATP-sensitive potassium channels, leading to oscillatory cellular depolarization and Ca2+ influx.773 While Ca2+ oscillations are considered the master regulator of pulsatile insulin secretion, fluorescent biosensors for ATP, glycolysis, cAMP, and PKA have shown that these signaling pathways also oscillate in β-cells and can modulate Ca2+ dynamics.308,519,774,775 Furthermore, multiplexed imaging using fluorescent biosensors has made it possible to observe how oscillations in different signaling molecules are temporally related and helps elucidate the crosstalk between pathways (multiplexing discussed further in section 6.2). Temporal differences between two oscillating signals are described by their phase shift, which is the temporal shift between the waveforms of the two signals. Signals that increase and decrease at the same time are “in phase”, whereas signals that oscillate at the same time but in opposite directions are “out of phase”. In the MIN6 β-cell line, simultaneous measurement of Ca2+ oscillations with PKA activity or cAMP concentrations using fluorescent biosensors showed that PKA and cAMP oscillate out of phase with Ca2+308,775 (Figure 11C). Similarly, simultaneous measurement of ATP and Ca2+ also showed out-of-phase oscillations.774 Both of these findings are interesting because both ATP and PKA have been shown to play a role in the formation of Ca2+ oscillations, but these regulatory mechanisms are not static. Furthermore, simultaneous measurement with high temporal resolution allows the identification of which signaling mechanism happens first, providing insights into possible causal relationships that lead to the oscillations. For example, upon glucose stimulation, ATP increases before Ca2+, suggesting that the increase in ATP causes the closure of the KATP channel and subsequently leads to cellular depolarization.774 Meanwhile, cross-correlation analysis of the subsequent ATP oscillations showed that Ca2+ began to increase before ATP levels began to drop, indicating that while the increase in ATP may be responsible for initiating Ca2+ oscillations, the subsequent ATP oscillations may in fact be driven by Ca2+ oscillations.774 Similar analyses have been performed with faster oscillations (on the order of seconds) in other cell types. For example, the IP3 indicator IRIS1 showed that IP3 increases prior to Ca2+ depolarization caused by glutamate stimulation of metabotropic glutamate receptor 5a (mGluR5a) in HeLa cells expressing exogenous mGluR5a.546 These studies highlight the importance of single-cell and high temporal resolution afforded by fluorescent biosensors in decoding the mechanisms and regulation of oscillatory signaling dynamics. 3.2.3. Bistability and Ultrasensitivity. The final types of signaling dynamics that we will discuss are the related dynamics of bistability and ultrasensitivity. Bistability generally describes a system that primarily exists in either of two almost discrete states, creating an “all-or-nothing” response to stimulation. Ultrasensitivity, on the other hand, refers to a signaling network that exhibits a “switchlike” response, where low levels of stimulation do not illicit a response, but after a certain threshold, the signaling response is very strong. Usually, this is observed as a dose response curve that has a hill coefficient greater than 1.776 Ultrasensitivity in signaling

sustained response with much less variability across the population. Experimental evaluation of this predicted effect found that pulse frequencies of growth factor stimulation that create a more “sustained-like” ERK response led to more differentiation of PC12 cells, more so for NGF but also observed with EGF. This agreed with earlier work that found that both continuous and pulsatile activation of ERK, using an optogenetic stimulation independent of growth factor, increased PC12 differentiation.763 More work will still be needed to identify how these cells are able to integrate the temporal differences between a transient and sustained response into cell fate and gene expression profiles. Additionally, the complexity of these dynamics and the high temporal resolution of fluorescent biosensors has made ERK signaling a prime candidate for interrogation using computational models, which is discussed in section 5.2.1. 3.2.2. Oscillations. Oscillatory signaling dynamics can be defined as a regular or semiregular variation in the signaling response (Figure 11C). Oscillations are physiologically critical for several functions such as heart rhythm, insulin secretion, and cell cycle timing.764 For oscillations to arise, the signaling network must have both negative feedback and delay in the negative feedback.765 One of the most common oscillatory dynamics observed has been in intracellular Ca2+ concentrations. These oscillations are driven by the ion channels that form a complex network of feedback mechanisms that can experience a delay due to their dependence on membrane potential or ion concentration.766 While these dynamics generally fall under electrophysiological signaling, enzymes and second messengers can be both required for oscillations and regulate the rate of oscillation.308,767 But oscillatory dynamics are not exclusive to Ca2+ signaling. For example, nuclear localization of the transcriptional regulator NF-κB has been shown to oscillate due to negative feedback caused by NF-κB promoting the expression of its inhibitor, IκB.768 In this system, the time required for both the nuclear translocation of NF-κB and its induction of IκB expression causes a delay between NF-κB activation and feedback inhibition, leading to the oscillatory behavior. Understanding the different mechanisms that can lead to and modulate oscillatory dynamics has been of intense interest for several fields. Oscillatory signaling dynamics are complex and may be difficult to observe without the use of fluorescent biosensors due to the temporal and single-cell resolution that may be required. For example, cardiac Ca2+ oscillations that regulate the rhythmic contraction of the heart are moderately fast (1− 10 Hz depending on species) and homogeneous across a population when under pacing.769 Other physiological systems that exhibit spontaneous or asynchronous Ca2+ oscillations across a cell population rely more heavily on single-cell measurements of dynamics.770,771 While fluorescent dyes such as Fura-2 are commonly used to study Ca2+ dynamics, GECIs can offer selectivity at the cellular and subcellular level that is generally not possible using cell-permeable synthetic dyes. For example, GECIs have been expressed in specific neuronal subtypes (e.g., pyramidal neurons)162 through the use of cellspecific promoters and have been targeted to specific microdomains through the use of targeting motifs.410 One particularly interesting system that relies on oscillatory signaling is the pulsatile release of insulin by pancreatic β-cells in response to glucose stimulation. Early studies using Ca2+ indicator dyes revealed that the elevation of glucose led to slow Ca2+ oscillations, with insulin release occurring in a similarly 11746


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Review

Therefore, the relocalization of Aurora B kinase at the centromere allows efficient activation due to the high concentration of Aurora B kinase, which remain active longer even when they diffuse away from the centromere, leading to a larger area of phosphorylation. Finally, recently published work by Yapo and colleagues observed, using AKAR2,507 that the nuclear PKA response in striatal neurons stimulated with a dopamine-like agonist exhibited an ultrasensitive response.788 They developed a computational model to examine potential mechanisms that may underlie this ultrasensitivity which suggested two possible feed-forward loops could be responsible: PKA activity promotes its translocation into the nucleus and PKA phosphorylation induced inhibition of nuclear phosphatases. All of these studies rely on the power of fluorescent biosensors to quantify signaling dynamics at the single-cell level in order to evaluate the bistability or ultrasensitivity of a signaling pathway that may have otherwise been hidden in population averages.

networks can be important for signaling pathways that may be critical for a response to stimuli that is resistant to noise, such as proliferation or apoptosis. While bistability is a broader term and usually requires ultrasensitivity in the signaling network,777 for the purposes of this review, these phenomena will be discussed interchangeably. There are several signaling network factors that can lead to ultrasensitivity, which can make it difficult to identify precisely which elements of a signaling network are responsible.778 Two common mechanisms that can lead to the switchlike behavior of an ultrasensitive response are positive feedback, in which a downstream enzyme promotes the activation of its upstream regulators, and multistep activation, wherein an enzyme requires activation via two or more independent steps.776−780 One example of an ultrasensitive signaling pathway is MAPK signaling, which contains positive feedback loops and involves a multistep activation process that requires phosphorylation at two separate sites.781,782 The ultrasensitive nature of some MAPK signaling pathways is critical to ensuring that commitment to cell fate decisions or apoptotic programs is not only resistant to errant initiation by noise but also robustly induced when required.781,783 Again, the signaling network topology alone is not sufficient to identify ultrasensitivity a priori,778 but the ability to study these dynamics at the single cell-level using fluorescent biosensors makes it possible to dissect the key factors that regulate these dynamics. JNK is a MAPK that is activated by cytokines and environmental stressors and regulates apoptosis.784 Early work in Xenopus oocytes had shown, using a very laborintensive process, that the JNK response to stimulation was a bistable system that exhibited all-or-none responses at the cellular level but appeared to produce a gradated response at the population level due to cell-to-cell variability.785 The development of a genetically encoded biosensor for monitoring JNK activity dynamics, JNKAR1, greatly improved the ability to study these heterogeneous cell responses in many different cell types.490 For example, exposing JNKAR1-expressing HeLa cells to a range of concentrations of the fungal antibiotic anisomycin, which induces ribotoxic stress and leads to the activation of JNK, revealed that cells either maximally responded to superthreshold concentrations or failed to respond at all to subthreshold concentrations490 (Figure 11D). These data showed that the JNK response to anisomycin has a hill coefficient greater than 9 and thus represents an ultrasensitive response, which conceptually makes sense for a cellular response that regulates apoptosis.490 Similar to the regulation of apoptosis by JNK, the regulation of cell division by Aurora B kinase has also been shown to exhibit bistability.786 Aurora B kinase activity has been shown to be critical for progression through cytokinesis and exhibits highly dynamic localization throughout cell division.787 Intriguingly, multiplexed imaging of an Aurora B kinase biosensor targeted to chromatin along with mCherry-labeled Aurora B kinase showed that during anaphase, Aurora B kinase was highly concentrated at centromeres, whereas the area of high Aurora B kinase phosphorylation extended much farther.786 Using in vitro experiments and computational models, Zaytsev et al. determined that this larger area of influence was due to bistability in the activation of Aurora B kinase. This bistability arises due to Aurora B kinase activation occurring mainly through trans-phosphorylation by other Aurora B kinase molecules, while deactivation by dephosphorylation occurs slowly due to a relatively low concentration of phosphatases.

3.3. Importance of Single-Cell Readouts

Because fluorescent biosensors are able to quantify signaling dynamics in both time and space, they are able to resolve the dynamic behaviors of individual cells. This property is integral for studying dynamics that are heterogeneous across cell populations and thus difficult to detect using population-based methods such as Western blotting. For example, the MIN6 oscillations discussed earlier are largely asynchronous, and the ability to simultaneously measure Ca2+ and the kinetics of other signaling proteins at the single-cell level using fluorescent biosensors was essential for understanding their dynamics.308,519,774,775 Indeed, single-cell resolution is often essential for revealing the true dynamics of a signaling pathway that would otherwise be obscured by cell-to-cell variability at the population level, such as ultrasensitive signaling.490,786,789 Meanwhile, single-cell studies can also reveal distinct behaviors among cell subsets, such as in recent work by Aoki et al., who used the FRET-based biosensor EKAREV to reveal the presence of stochastic pulses of ERK activity within individual cells, which were found to have a paracrine activating effect on neighboring cells.790 Even in cases such as studies of the cell cycle, where it is possible to synchronize cells to the same point in the cell cycle, the rate at which each cell progresses through the cycle will nevertheless exhibit significant cell-tocell variability, and quantification of signaling kinetics at the single-cell level may still be required. Therefore, a genetically encodable biosensor was used to measure Cyclin B1-CDK1 activity through the cell cycle and elucidate cell-to-cell variation during this process.436,462 Studying these processes at the single-cell level thus enables a greater understanding of signaling networks than can be achieved through more traditional means. 3.4. How Fast Can We Go?

In most of the examples discussed thus far, a temporal resolution on the order of seconds was adequate to resolve the dynamics of interest. However, numerous signaling processes are known to exhibit very rapid kinetics, such as GPCR activation 5 7 9 , 5 8 2 , 5 8 4 and neuronal depolarization spikes,164,273,402 and a much finer time resolution is thus required in order to faithfully capture the dynamics of these processes. For example, the biosensors developed to study GPCR activation rates, Ca2+ dynamics, and membrane potential are able to achieve temporal resolutions on the order of 100 ms, 1.5 ms, and

Genetically Encoded Fluorescent Biosensors ... - ACS Publications

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